JURNAL TEKNIK KOMPUTER AMIK BSI
type: channel
description: Jurnal Teknik Komputer AMIK BSI is a scientific journal published by LPPM Bina Sarana Informatics University. This journal contains research scientific works with the theme: Networking, Embedded Systems, Science Applications, Interactive Animation, Image Processing, Expert Systems, Computer Systems, Soft Computing, Web Programming, Mobile Programming, Games Programming, Data Mining, and Decision Support Systems . Issue Frequency: 2 times a year (January and July). First published in 2015, and already has ISSN: 2442-2436 (print), and 2550-0120 (online). As well as being accredited by Sinta 4 with number: 177/E/KPT/2024, dated Oct 13, 2024.
publisher: LPPM Universitas Bina Sarana Informatika
language: en
publication name: JURNAL TEKNIK KOMPUTER AMIK BSI
issn: 2442-2436
items: Early Detection of GDM in First-Trimester Pregnant Women Using SVM Classifier
items: Chicken Egg Price Forecasting in East Java Using LSTM and GRU
items: A Retrieval-Augmented Generation-Based Chatbot for Optimizing Digital Patient Triage in E-Health Systems
items: Design and Development of Kumpul.in Mobile Application for Schedulled Wasted Collection System
items: Evaluasi User Experience Pada Pengguna Aplikasi Maxim Indonesia Menggunakan Metode User Experience Questionnaire (UEQ)
items: Clustering Penduduk Kurang Mampu Di Desa Mekar Baru Menggunakan Algoritma K-Means
items: Analysis of WhatsApp Social Media Application Services on User Satisfaction Using the PIECES Framework Method
items: Penanganan Overfitting pada Klasifikasi Berita Hoax berbasis Neural Networks dengan Dropout dan Regularization
items: Perbandingan SVM dan LSTM Untuk Memprediksi Gangguan Kecemasan (Anxiety Disorder) Berdasarkan Cuitan di Platform Aplikasi X (Twitter)
items: Analisis Manajemen Proyek Sistem Informasi Pengolahan Persediaan DataObat di Klinik Cirendeu Medika Tangerang
items: Optimasi Naïve Bayes menggunakan Seleksi Fitur Forward Selection untuk Analisis Sentimen Kendaraan Listrik
items: Pemodelan Sistem Penyiraman Cerdas Bergerak Pada Budidaya Jamur Tiram
items: Analysis Usability Terhadap Kepuasan Pengguna MyIndihome dengan Metode Use Quesionnaeri di Wilayah Kelurahan Rawang
items: FaceVoting: e-Voting Berbasiskan Pengenalan Wajah
items: Penerapan Metode Algoritma C5.0 Untuk Klasifikasi Pemberian Kredit KUR UMKM Pada PT Pegadaian
items: Application of Random Forest algorithm to classify credit status ofKPR customers at Bank BTN based on machine learning
items: Application of Prediction Models Based on Moving Average, Exponential Smoothing and Trend Analysis on Indonesian Palm Oil Exports
items: Quality Of SMK E Rapor System Version 7.0.0 Using Webqual 4.0 Method At SMKS Manggala Tangerang City
items: Analysis of the Influence of System Quality, Information Quality, Service Quality on User Satisfaction and Continuance Use Intention in the Malang Mbois Application
items: Comparison of News Text Summarization Using NLTK and TextRank Based on Python Programming
items: Development of an Android Application to Identify Fish Species Using Kotlin-based Android Studio
items: Blockchain's Impact on Coffee Supply Chains: A Systematic LiteratureReview
items: Employee Payroll Accounting Application Using Extreme ProgrammingDevelopment Method
items: WebQual 4.0 Analysis of Indonesian Election and Government Websites
items: Intelligent Obstacle Detection And Warning System For Railroad Crossings Using Yolov8 And Bytetrack With Signal-Triggered Response
items: Design and Development of Mobile Based Laundry Business Application

Early Detection of GDM in First-Trimester Pregnant Women Using SVM Classifier
type: item
description: Indonesia is among the top five countries in the world with the highest number of people living with diabetes, including those in the productive age group and pregnant women. Gestational Diabetes Mellitus (GDM) is a common metabolic complication during pregnancy and is generally diagnosed at 24–28 weeks of gestation. Delayed detection of GDM can limit opportunities for early intervention and increase the risk of complications for both the mother and the fetus. Therefore, an early detection approach is necessary, particularly during the first trimester of pregnancy. The development of Artificial Intelligence (AI), especially Machine Learning (ML), provides opportunities to develop disease prediction models based on clinical data. One algorithm that has demonstrated strong performance in classification tasks is the Support Vector Machine (SVM). This study aims to evaluate the performance of an SVM model using the Confusion Matrix and Area Under the Curve (AUC) in the early detection of GDM among first-trimester pregnant women. The research methodology includes the collection and preprocessing of clinical data, implementation of the algorithm, and testing or evaluation of model performance. The results show an accuracy of 71% and an AUC value of 76%. This study is expected to contribute to the early detection of GDM in pregnant women, thereby helping to reduce the risk of diabetes in children after birth.
subject: Articles
  topic: section
subject: GDM
  topic: keywords
subject: Pregnant Women
  topic: keywords
subject: Classification
  topic: keywords
subject: SVM
  topic: keywords
subject: AUC
  topic: keywords
creator: Achmad Baroqah Pohan
creator: Siti Masripah
creator: Lestari Yusuf
creator: Dini Nurlaela
creator: Lila Dini Utami
creator: Sri Wasiyanti
rights: Copyright (c) 2026 Achmad Baroqah Pohan, Siti Masripah, Lestari Yusuf, Dini Nurlaela, Lila Dini Utami, Sri Wasiyanti https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
doi: 10.31294/jtk.v12i2.12783

Chicken Egg Price Forecasting in East Java Using LSTM and GRU
type: item
description: Chicken egg prices in East Java Province fluctuate considerably due to external factors, including day-old chick prices, livestock feed prices, and egg supply, making accurate price forecasting essential for supporting decision-making. This study compares the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for forecasting chicken egg prices and implements the best-performing model in a web-based forecasting system. Historical data from 2020 to 2025 were collected from SIMPONI-Ternak and Statistics Indonesia and processed through data transformation, cleaning, missing value handling, normalization, dataset splitting, and hyperparameter optimization using Bayesian Optimization. Both recursive and non-recursive forecasting approaches were evaluated using Mean Absolute Error, Root Mean Square Error, and Mean Absolute Percentage Error. The results indicate that the recursive approach consistently outperformed the non-recursive approach for both models. The Recursive LSTM achieved the best performance with a Mean Absolute Error of 224.80, a Root Mean Square Error of 363.04, and a Mean Absolute Percentage Error of 0.84%, outperforming the Recursive GRU model. Validation using actual price data for January 2026 confirmed that the Recursive LSTM produced the most accurate and stable forecasts. The selected model was successfully implemented in a Flask-based web application to provide practical and accessible chicken egg price forecasting.
subject: Articles
  topic: section
subject: Chicken egg price,
  topic: keywords
subject: forecasting,
  topic: keywords
subject: LSTM,
  topic: keywords
subject: GRU
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Fadhila Amalia Fatihah
creator: Dwiretno Istiyadi Swasono
rights: Copyright (c) 2026 Fadhila Amalia Fatihah, Dwiretno Istiyadi Swasono https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
doi: 10.31294/jtk.v12i2.13698

A Retrieval-Augmented Generation-Based Chatbot for Optimizing Digital Patient Triage in E-Health Systems
type: item
description: Patient triage is a critical component in healthcare delivery systems that determines care priorities based on medical condition severity. Conventional triage systems often face limitations including assessment subjectivity, initial diagnosis inaccuracy, and slow response times, which can impact patient clinical outcomes. This research proposes the implementation of a Retrieval-Augmented Generation (RAG)-based chatbot to optimize digital patient triage processes in e-health systems. The RAG method integrates the generative capabilities of Large Language Models (LLMs) with external medical knowledge retrieval mechanisms from validated databases, thereby reducing AI hallucinations and improving clinical recommendation accuracy. The developed system employs a vector database architecture to store medical document embeddings, similarity search algorithms for relevant context retrieval, and LLMs to generate accurate and contextual responses. Expected outcomes include improved accuracy in patient risk stratification, reduced waiting times, and enhanced patient satisfaction through timely and appropriate care navigation. The system is also expected to reduce healthcare worker burden through automation of documentation processes and evidence-based clinical decision-making.
subject: Articles
  topic: section
subject: Retrieval-Augmented Generation,
  topic: keywords
subject: E-Health Chatbot,
  topic: keywords
subject: Digital Triage,
  topic: keywords
subject: Large Language Model,
  topic: keywords
subject: Health Information System
  topic: keywords
creator: Muhammad Iqbal
creator: Siti Nurdiani
creator: Lisnawanty Lisnawanty
creator: Safitri Linawati
creator: Muhammad Ifan Rifani Ihsan
creator: Muhammad Fahmi Julianto
rights: Copyright (c) 2026 Muhammad Iqbal, Siti Nurdiani, Lisnawanty Lisnawanty, Safitri Linawati, Muhammad Ifan Rifani Ihsan, Muhammad Fahmi Julianto https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
doi: 10.31294/jtk.v12i2.12806

Design and Development of Kumpul.in Mobile Application for Schedulled Wasted Collection System
type: item
description: Permasalahan pengelolaan sampah di tingkat masyarakat sering disebabkan oleh kurangnya koordinasi antara petugas pengambil sampah dan warga, sehingga jadwal pengambilan sampah tidak berjalan optimal. Penelitian ini bertujuan merancang dan membangun aplikasi mobile Kumpul.in sebagai sistem informasi pengambilan sampah terjadwal pada KSM TPS Ceria Sumampir. Metode pengembangan sistem menggunakan model Waterfall yang meliputi analisis kebutuhan, perancangan, implementasi, dan pengujian. Aplikasi dikembangkan menggunakan Flutter dengan Firebase sebagai basis data real-time. Hasil penelitian menunjukkan bahwa aplikasi mampu meningkatkan efektivitas koordinasi dan keteraturan jadwal pengambilan sampah hingga 90% dibandingkan sistem manual. Aplikasi ini diharapkan menjadi solusi digital pengelolaan sampah berbasis masyarakat yang lebih efisien dan berkelanjutan.
subject: Articles
  topic: section
subject: aplikasi mobile, pengelolaan sampah, sistem informasi, Waterfall, KSM
  topic: keywords
creator: Nurfirmansyah Putra
creator: Ina Maryani
creator: Suripah Suripah
creator: Cyndira Galuhwardani
creator: Deny Kurniawan
creator: Dedi Triyanto
rights: Copyright (c) 2026 Nurfirmansyah Putra, Ina Maryani, Suripah Suripah, Cyndira Galuhwardani, Deny Kurniawan, Dedi Triyanto https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
doi: 10.31294/jtk.v12i2.12798

Evaluasi User Experience Pada Pengguna Aplikasi Maxim Indonesia Menggunakan Metode User Experience Questionnaire (UEQ)
type: item
description: Pengalaman pengguna (User Experience) merupakan aspek yang sangat penting dalam kesuksesan sebuah aplikasi. Pengguna yang memiliki pengalaman positif dengan sebuah aplikasi cenderung akan lebih sering menggunakan aplikasi dan lebih mungkin merekomendasikannya kepada orang lain. Oleh karena itu evaluasi terhadap pengalaman pengguna pada suatu aplikasi merupakan hal yang sangat penting untuk memastikan bahwa pengguna memiliki pengalaman yang baik dan memuaskan. Metode evaluasi yang digunakan pada penelitian ini adalah User Experience Questionnaire. Adapun aspek-aspek yang dievaluasi meliputi Attractiveness (daya tarik), Perspicuity (kejelasan), Efficiency (efisiensi), Dependability (ketepatan), Stimulation (stimulasi), Novelty (kebaruan). Berdasarkan hasil penelitian diperoleh hasil bahwa penelitian ini berhasil mengevaluasi pengalaman pengguna pada aplikasi Maxim Indonesia menggunakan User Experience Questionnaire (UEQ). Berdasarkan hasil benchmark didapatkan nilai pada masing-masing aspek UEQ dengan menggunakan UEQ Data Analysis Tool meliputi aspek attractiveness (mean 1,19), aspek perspicuity (mean 1,31), aspek efficiency (mean 1,21), aspek dependability (mean 1,14), aspek stimulation (mean 1,15), aspek novelty (mean 0,95) sehingga mendapatkan nilai above average (diatas rata-rata). Berdasarkan hasil evaluasi juga penelitian ini memberikan rekomendasi perbaikan kepada aplikasi Maxim Indonesia untuk meningkatkan pengalaman pengguna melalui area yang perlu diperbaiki meliputi efficiency, dependability stimulation dan novelty dengan tujuan mendapatkan nilai positif yang lebih tinggi dari pengguna aplikasi. Berdasarkan hasil tersebut maka penelitian ini berhasil memberikan kontribusi penting dalam meningkatkan pengalaman pengguna pada aplikasi Maxim Indonesia dan memberikan panduan untuk perbaikan yang lebih lanjut guna mencapai tingkat kepuasan yang lebih baik.
subject: Articles
  topic: section
subject: Evaluasi pengalaman pengguna
  topic: keywords
subject: , Maxim Indonesia
  topic: keywords
subject: User Experience Questionnaire
  topic: keywords
creator: Muhammad Rifqi Adli
creator: Wahyudin Wahyudin
creator: Syamsul Bahri
rights: Copyright (c) 2024 Muhammad Rifqi Adli, Wahyudin Wahyudin, Syamsul Bahri https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 171
ending page: 177
doi: 10.31294/jtk.v10i2.12379

Clustering Penduduk Kurang Mampu Di Desa Mekar Baru Menggunakan Algoritma K-Means
type: item
description: Kemiskinan merupakan masalah yang telah lama ada dan belum berhasil diselesaikan oleh pemerintah. Meskipun berbagai upaya, seperti program bantuan sosial tunai (BLT), telah dilakukan, masih terdapat tantangan dalam pelaksanaannya. Salah satu kendala yang dihadapi pemerintah daerah di Desa Mekar Baru adalah ketidak merataan dan ketidaktepatan sasaran dalam distribusi bantuan sosial. Algoritma K-Means, yang merupakan salah satu algoritma paling populer dan sederhana, digunakan untuk mengelompokkan data penduduk kurang mampu menjadi beberapa klaster. Berdasarkan hasil evaluasi, diperoleh empat klaster: klaster 1 (sangat tidak mampu) dengan 114 data (36,8% dari total data), klaster 0 (sangat mampu) dengan 89 data (28,8%), klaster 3 (mampu) dengan 83 data (26,8%), dan klaster 2 (sangat mampu) dengan 23 data (7,4%). Klaster tersebut diurutkan dari yang memiliki jumlah terbesar hingga terkecil. Penelitian ini bertujuan untuk mengembangkan sebuah aplikasi yang dapat mengidentifikasi klaster dari dataset yang dianalisis menggunakan metode K-Means.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: klastering
  topic: keywords
subject: K-means
  topic: keywords
subject: Penduduk Kurang mampu
  topic: keywords
creator: Egy Andryan
creator: Asrul Abdullah
creator: Putri Yuli Utami
rights: Copyright (c) 2024 Egy Andryan, Asrul Abdullah, Putri Yuli Utami https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 163
ending page: 170
doi: 10.31294/jtk.v10i2.12377

Analysis of WhatsApp Social Media Application Services on User Satisfaction Using the PIECES Framework Method
type: item
description: Technological developments are increasingly advancing and shifting towards a more modern era. Today, social media plays an essential role in society, facilitating various activities such as information exchange and retrieval in a faster, easier, and more effective way. Social media can now be utilized for various communication purposes and is widely adopted by organizations, corporations, governments, and social institutions. This study applies a quantitative method using the PIECES Framework to evaluate the service quality of the WhatsApp application. The study population was obtained through a Google Form distributed to 100 respondents consisting of students and other WhatsApp users. The analysis results show an average customer satisfaction score of 4.19, indicating high satisfaction in the Performance variable. The evaluation across all PIECES Framework factors yielded satisfactory results, affirming WhatsApp’s positive contribution in enhancing the service standards of social media applications.
subject: Articles
  topic: section
subject: WhatsApp
  topic: keywords
subject: Users
  topic: keywords
subject: PIECES
  topic: keywords
subject: Framework
  topic: keywords
creator: Hanggoro Aji Al Kautsar
creator: Lukmanul Hakim
rights: Copyright (c) 2026 Hanggoro Aji Al Kautsar, Lukmanul Hakim https://creativecommons.org/licenses/by-sa/4.0
date: 2026-07-31
publication date: 2026-07-31
doi: 10.31294/jtk.v12i2.9449

Penanganan Overfitting pada Klasifikasi Berita Hoax berbasis Neural Networks dengan Dropout dan Regularization
type: item
description: Penelitian ini mengevaluasi efektivitas berbagai teknik deteksi hoaks di Indonesia menggunakan model klasifikasi teks dengan dua ukuran dataset berbeda, yaitu 250 dan 650 sampel. Hoaks di media sosial memiliki dampak signifikan pada masyarakat, sehingga deteksi yang akurat sangat penting. Penelitian ini menguji tiga algoritma machine learning—ID CNN, Bi-LSTM, dan LSTM—dengan teknik regulasi seperti original, regularization, dan dropout. Hasil penelitian menunjukkan bahwa teknik regularisasi pada ID CNN memberikan akurasi tertinggi pada dataset 250 sampel, sementara Bi-LSTM dengan teknik original mencapai akurasi tertinggi pada dataset yang sama. Dataset yang lebih besar (600 sampel) menunjukkan bahwa teknik regularisasi pada ID CNN tetap stabil, sedangkan teknik dropout memberikan hasil yang bervariasi. Analisis menggunakan confusion matrix dan grafik learning menunjukkan adanya overfitting pada model, terutama pada dataset yang lebih kecil. Temuan ini menegaskan pentingnya penerapan teknik regulasi untuk mengurangi overfitting dan meningkatkan generalisasi model dalam deteksi hoaks. Penelitian ini memberikan kontribusi pada pengembangan sistem deteksi hoaks yang lebih efektif di Indonesia.
subject: Articles
  topic: section
subject: Deteksi hoaks
  topic: keywords
subject: klasifikasi teks
  topic: keywords
subject: ID CNN
  topic: keywords
subject: Bi-LSTM
  topic: keywords
subject: LSTM
  topic: keywords
subject: regularisasi
  topic: keywords
subject: dropout
  topic: keywords
subject: overfitting
  topic: keywords
creator: Ridwan Ilyas
creator: fatan Kasyidi
creator: Maulidina Norick Eriyadi
rights: Copyright (c) 2024 Ridwan Ilyas, fatan Kasyidi, Maulidina Norick Eriyadi https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 156
ending page: 162
doi: 10.31294/jtk.v10i2.12275

Perbandingan SVM dan LSTM Untuk Memprediksi Gangguan Kecemasan (Anxiety Disorder) Berdasarkan Cuitan di Platform Aplikasi X (Twitter)
type: item
description: Anxiety Disorder adalah gangguan kecemasan yang menyerang kesehatan mental seseorang sehingga bisa mengganggu aktivitas. Pengaruh media sosial menjadi salah satu adanya pemicu gangguan kecemasan, dimana media sosial dijadikan objek untuk meluapkan perasaan yang dialami pengguna, contohnya Twitter atau aplikasi X. Penelitian ini dilakukan untuk memprediksi adanya anxiety disorder pada pengguna Twitter dilihat berdasarkan cuitan yang ada dengan menggunakan dua model, yaitu Support Vector Machine (SVM) dan Long Short-Term Memory (LSTM) sehingga bisa dibandingkan mana yang lebih baik diantara dua metode tersebut. Data yang digunakan merupakan data hasil dari crawling yang kemudian dilakukan beberapa pemrosesan sehingga bisa diolah sesuai dengan medel yang ada. Hasil dari penelitian ini menunjukkan bahwa untuk memprediksi anxiety disorder menggunakan Long Short-Term Memory (LSTM) lebih unggul daripada Support Vector Machine (SVM) dari 3 matriks perhitungan yang ada, yaitu precision, recall, dan f1-score dengan nilai 75%. Sedangkan, untuk metode Support Vector Machine (SVM) hanya unggul dalam perhitungan nilai akurasi, yaitu 81%.
subject: Articles
  topic: section
subject: Anxiety Disorder
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
creator: Nurochman Nurochman
creator: Luthfia Ashiilah
rights: Copyright (c) 2024 Nurochman Nurochman, Luthfia Ashiilah https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 147
ending page: 155
doi: 10.31294/jtk.v10i2.12274

Analisis Manajemen Proyek Sistem Informasi Pengolahan Persediaan DataObat di Klinik Cirendeu Medika Tangerang
type: item
description: Transformasi digital di bidang kesehatan mulai gencar dilaksanakan. Penyediaan sistem informasi Kesehatan yang terintegrasi coba dicanangkan untuk perbaikan tata kelola kesehatan. Namun, di Klinik Cirendeu Medika Tangerang, belum terdapat perbaikan yang maksimal akan hal tersebut, salah satu contohnya adalah pencatatan obat yang masih dilakukan secara manual sehingga membutuhkan waktu yang lama dan rentan akan kesalahan. Dalam mendukung tranformasi digital tersebut maka Klinik Cirendeu Medika Tangerang mulai menerapkan digitalisasi dalam menjalankan proses bisnis nya. Pencatatan untuk persediaan obat, penerimaan obat, hingga pengeluaran obat akan dilakukan dengan mengakses website sehingga dapat terdokumentasi dengan baik. Penyusunan untuk kebutuhan tersebut dapat terlaksana apabila tertuang dalam perencanaan yang baik. Perencanaan tersebut dapat diakomodir melalui penyusunan Proyek Sistem Informasi yang terencana, terpadu, sehingga dapat menghasilkan keluaran yang efisien dan tepat sasaran.
subject: Articles
  topic: section
subject: Transformasi Digital
  topic: keywords
subject: Tata Kelola
  topic: keywords
subject: Proyek Sistem Informasi
  topic: keywords
creator: Normah Normah
creator: Siti Nurajizah
creator: Rizqitha Maula
creator: Divika Namastute
creator: Alifia Ramadhanti
rights: Copyright (c) 2024 Normah Normah, Siti Nurajizah, Rizqitha Maula, Divika Namastute, Alifia Ramadhanti https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 139
ending page: 146
doi: 10.31294/jtk.v10i2.12273

Optimasi Naïve Bayes menggunakan Seleksi Fitur Forward Selection untuk Analisis Sentimen Kendaraan Listrik
type: item
description: Isu pemanasan global menjadi perhatian utama para pemimpin di beberapa negara termasuk Indonesia. Salah satu kontribusi untuk mengurangi emisi karbon yang berakibat pada pemanasan global yaitu dengan menggulirkan kebijakan kendaraan listrik. Pro kontra kebijakan kendaraan Listrik di Indonesia telah merambah ke berbagai ruang diskusi publik termasuk media social X (Twitter). Berdasakan hal tersebut penelitian ini bermaksud untuk menganalisis sentimen Masyarakat pada media social X tentang kebijakan kendaraan Listrik dilakukan klasifikasi dengan memanfaatkan algoritma naïve bayes berbasis forward selection. Berdasarkan pengujian model klasifikasi naïve bayes ditambahkan forward selection dihasilkan accuracy sebesar 82,11% dengan nilai AUC 0,801. Ada kenaikan nilai accuracy sebesar 2,67% dibandingkan tanpa menggunakan seleksi fitur forward selection yang hanya mencapai accuracy dikisaran 79,43 % dengan nilai AUC 0,639. Peneliti menarik Kesimpulan bahwa penggunaan seleksi fitur menggunakan forward selection dapat berkontribusi meningkatkan algoritma naïve bayes dalam klasifikasi sentimen pada kendaraan yang bertenaga listrik.
subject: Articles
  topic: section
subject: Naïve bayes
  topic: keywords
subject: Analisis sentimen
  topic: keywords
subject: Forward Selection
  topic: keywords
creator: Salman Alfarizi
creator: Deni Gunawan
creator: Hasan Basri
creator: Alif Rizqi Mulyawan
creator: Nurul Ichsan
rights: Copyright (c) 2024 Salman Alfarizi, Deni Gunawan, Hasan Basri, Alif Rizqi Mulyawan, Nurul Ichsan https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 131
ending page: 138
doi: 10.31294/jtk.v10i2.12272

Pemodelan Sistem Penyiraman Cerdas Bergerak Pada Budidaya Jamur Tiram
type: item
description: Integrasi sistem kendali jarak jauh telah menjadi aspek krusial dalam evolusi teknologi digital. Sistem tersebut memegang peran dan fungsi yang bervariasi, untuk memenuhi banyak ragam kebutuhan. Pada masa periode Revolusi Industri 4.0 seiring dengan lajunya Pertanian 4.0, penerapan teknologi ini semakin meluas, salah satu riset yang telah dilakukan pada artikel ini adalah proses penyiraman atau pengabutan pada ruang budidaya secara otomatis bergerak. Fokus masalah penyiraman didasarkan pada permasalahan terhadap kurangnya pemerataan hasil penyiraman, sehingga memunculkan inovasi ide fitur adaptif penyiraman otomatis bergerak. Pengembangan riset diawali dengan melakukan integrasi teknologi sensor suhu, kelembapan dan intensitas cahaya, dilengkapi dengan perangkat kendali utama (mikrokendali), dan perangkat aktuator berdaya DC. Selain itu sistem yang diusulkan ini, menerapkan algoritma fuzzy yang lebih adaptif terhadap perubahan fenomena alamiah dan iklim, sesuai kebutuhannya, dimana suhu antara 15-33 o Celcius, kelembapan antara 47-99% serta intensitas cahaya pada nilai rentang 80-800 lumens. Proses pengamatan, pemodelan dan pengujian sistem berjalan dalam 1 periode tanam Jamur Tiram, dan telah diterapujikan di lingkungan sebenarnya di wilayah Kota Semarang. Sinergi berbagai teknologi, didampingi oleh algoritma terapan berbasis pembelajaran mesin fuzzy pada sistem ini telah mampu menghasilkan sebuah perangkat yang inovatif cerdas kekinian, sesuai kebutuhan penggunaannya, dan berpotensi luas untuk dapat diterapkan pada beragam lingkup budidaya tanaman hortikultura maupun fortikultura lainnya.
subject: Articles
  topic: section
subject: Sistem Cerdas
  topic: keywords
subject: Penyiraman Bergerak
  topic: keywords
subject: , Fuzzy Adaptif
  topic: keywords
subject: Autosprayer
  topic: keywords
creator: Herny Februariyanti
creator: Eddy Nurraharjo
creator: Muji Sukur
creator: Firman Ardiansyah Ekoanindiyo
rights: Copyright (c) 2024 Herny Februariyanti, Eddy Nurraharjo, Muji Sukur, Firman Ardiansyah Ekoanindiyo https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 123
ending page: 130
doi: 10.31294/jtk.v10i2.12270

Analysis Usability Terhadap Kepuasan Pengguna MyIndihome dengan Metode Use Quesionnaeri di Wilayah Kelurahan Rawang
type: item
description: MyIndiHome adalah aplikasi resmi dari IndiHome yang merupakan penyedia layanan digital dan internet terlengkap di Indonesia. Layanan Internet Indihome siap menjangkau pengguna dari sabang sampai merauke. memungkinkan pengguna mengakses fungsi layanan IndiHome dengan mudah. Namun ada kendala dari aplikasi myIndiHome yang mengalami ketidak konsistenan informasi seperti kecepatan internet yang berada di aplikasi dengan kecepatan yang seharusnya didapatkan, sehingga dapat berimbas pada tingkat kepuasaan pengguna. Untuk mengetahui sejauh mana aplikasi myindihome dapat digunakan pengguna untuk mencapai tujuan tertentu dengan efektif, efisien dan pengguna menjadi puas dalam kontek penggunaan, maka perlu dilakukan pengukuran usability dari aplikasi tersebut. Dalam penelitian ini bertujuan untuk mengetaհui tingkat kepuasan pengguna pada aplikasi myIndiHome bagi pelanggan IndiHome di wilayah Kelurahan Rawang Kota Padang dengan metode USE Questionnaire. yang meliputi aspek Usefulness, Ease of Use, Ease of Learning, dan Satisfaction pada aplikasi myIndiHome dengan SPSS. Tingkat Usability pada aplikasi myIndiHome bagi pengguna IndiHome di wilayah Kelurahan Rawang mendapatkan rhitung > 0.197 dalam kategori kelayakan bermanfaat.
subject: Articles
  topic: section
subject: Usability
  topic: keywords
subject: Use Questionnaire
  topic: keywords
subject: Aplikasi MyIndiHome
  topic: keywords
creator: Hardiyan Hardiyan
creator: Ihsan Theo Saputra
rights: Copyright (c) 2024 Hardiyan Hardiyan, Ihsan Theo Saputra https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 118
ending page: 122
doi: 10.31294/jtk.v10i2.12267

FaceVoting: e-Voting Berbasiskan Pengenalan Wajah
type: item
description: FaceVoting adalah sistem pemungutan suara elektronik yang menggunakan teknologi pengenalan wajah untuk mengautentikasi pemilih, sehingga meningkatkan keamanan dan efisiensi proses pemungutan suara. Penelitian ini menunjukkan integrasi algoritma Haar Cascade, yang mampu mendetekesi objek secara cepat dan akurat, khususnya untuk pengenalan wajah dalam berbagai kondisi. Melalui rangakaian uji coba, sistem terbukti sangat efektif dalam mengenali wajah di berbagai jarak, sudut, dan ekspresi, meskipun memiliki beberapa keterbatasan dalam mendeteksi wajah yang terhalang atau tidak terdaftar. Evaluasi ekstensif melibatkan perekaman wajah pada jarak 30 cm dan 100 cm, menghasilkan skor deteksi yang konsisten sebesar 0.2586751709411996, yang menunjukkan kekokohan sistem. Selain itu, perekaman wajah yang divalidasi di bawah sudut yang berbeda menunjukkan kemampuan sistem untuk mempertahankan akurasi, dengan variasi skor yang minimal, menonjolkan adaptabilitasnya terhadap posisi pemilih yang berbeda. Pengujian lebih lanjut pada objek yang beragam dan ekspresi wajah menunjukkan presisi sistem dalam skenario dunia nyata, meskipun ada tantangan dalam mengenali objek non-manusia, wajah yang sebagian terhalang, dan wajah orang lain. Sekitar 96.5% dari 110 partisipan menyatakan kepuasan mereka terhadap fitur pengenalan wajah, menekankan keyakinan mereka akan potensinya untuk mengamankan dan mempermudah proses pemilihan. Studi ini mengonfirmasi kelayakan teknologi pengenalan wajah dalam sistem e-voting, memberikan peningkatan yang signifikan atas metode tradisional dalam hal aksesibilitas, keandalan, dan pencegahan kecurangan, sehingga mendukung proses pemilihan yang lebih inklusif dan transparan.
subject: Articles
  topic: section
subject: Pengenalan wajah
  topic: keywords
subject: e-voting
  topic: keywords
subject: Haar Cascade
  topic: keywords
creator: Rahmalia Syahputri
creator: Berkat Fa’atulo Halawa
creator: Sherli Trisnawati
creator: Nurfiana Nurfiana
creator: taufik taufik
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 107
ending page: 117
doi: 10.31294/jtk.v10i2.12222

Penerapan Metode Algoritma C5.0 Untuk Klasifikasi Pemberian Kredit KUR UMKM Pada PT Pegadaian
type: item
description: KUR atau dikenal dengan Kredit Usaha Rakyat merupakan program pemerintah yang bertujuan untuk memberikan bantuan usaha kepada rakyat kecil yakni yang memilki Usaha (UMKM). Pemberian Kredit juga dapat merujuk pada situasi di mana satu pihak memberikan uang atau jasa kepada pihak lain. Setiap pemberian kredit kepada nasabah terdapat kemungkinan gagal bayar. Kondisi gagal bayar dapat merugikan yang memberikan kredit. Kondisi gagal bayar ini selain merugikan perusahaan pemberi kredit juga berdampak pada kinerja perusahaan. Untuk mengatasi risiko gagal bayar, penting bagi perusahaan pemberi kredit ntuk melakukan analisis kredit yang cermat, memberikan edukasi keuangan kepada nasabah, dan memiliki strategi yang efektif. Selain itu, memantau dan memperbarui kondisi keuangan nasabah secara berkala juga merupakan langkah penting dalam mencegah kegagalan pembayaran. Data mining merupakan suatu metode untuk menemukan pengetahuan dari tumpukan data. Dalam penelitian ini data mining digunakan untuk mengatasi masalah resiko gagal bayar pada kelayakan kredit usaha dengan melibatkan data untuk mengidentifikasi pola dan faktor-faktor yang dapat memprediksi potensi gagal bayar. Dengan menggunakan metode algoritma 5.0 alam pengolahan data dapat digunakan untuk mengotomatisasi sebagian besar proses penilaian kredit yang dapat menghemat waktu dan biaya. Penerapan data mining dalam lembaga pegadaian memungkinkan pengambilan keputusan yang lebih tepat berdasarkan data historis dan analisis yang mendalam, sehingga membantu mengurangi risiko dan meningkatkan efisensi operasional pada perusahan pemberi kredit. Penerapan metode Algoritma C5.0 membantu dalam mengidentifikasi faktor-faktor kritis yang mempengaruhi kelayakan kur syariah UMKM, seperti profil pelanggan, karakteristik usaha, dan kinerja keuangan. Hasil penenlitian ini adalah diperoleh nilai akurasi metode algoritma C5.0 untuk pengolahan dataset pengajuan pinjaman dengan nilai akurasi 95,56%,selain itu terdapat model pohon keputusan…
subject: Articles
  topic: section
subject: Kredit
  topic: keywords
subject: Data Mining
  topic: keywords
subject: ,Algoritma 5.0
  topic: keywords
creator: Hilda Amalia
creator: Moranta Timotius
creator: sriyadi sriyadi
creator: Yunita Yunita
creator: Achmad Baroqah Pohan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-sa/4.0
date: 2024-07-14
publication date: 2024-07-14
starting page: 101
ending page: 106
doi: 10.31294/jtk.v10i2.12221

Application of Random Forest algorithm to classify credit status ofKPR customers at Bank BTN based on machine learning
type: item
description: In the banking sector, this study is very suitable for determining and improving accuracy and determining credit status classification. This study aims to apply the Exploratory Data Analysis (EDA) method in supporting credit status classification at PT. Bank Tabungan Negara KCP Lubuklinggau Persero Tbk. Exploratory Data Analysis (EDA) as data exploration and Machine Learning Algorithms such as Random Forest as modeling in determining classification. The results show that the Exploratory Data Analysis (EDA) method successfully determines data patterns, while Random Forest in modeling achieves accuracy, recall, Precision, F1-Score of 100% in predicting the credit status of KPR customers. This method is expected to be useful in making decisions on more accurate credit status by the bank.
subject: Articles
  topic: section
subject: EDA
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Credit Classification KPR
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Maysade Fitri
creator: Ahmad Sobri
creator: Fido Rizki
rights: Copyright (c) 2025 Maysade Fitri, Ahmad Sobri, Fido Rizki https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 70
ending page: 79
doi: 10.31294/jtk.v11i1.12116

Application of Prediction Models Based on Moving Average, Exponential Smoothing and Trend Analysis on Indonesian Palm Oil Exports
type: item
description: Palm oil is a strategic commodity for Indonesia, significantly contributing to state revenue and foreign exchange. In 2022, its export value reached USD 33.7 billion. Accurate forecasting of palm oil exports is crucial due to fluctuating market conditions influenced by global demand, prices, and government policies. However, existing studies on forecasting Indonesian palm oil exports are limited, with most research focusing on other agricultural commodities. This study applies Moving Average, Exponential Smoothing, and Trend Analysis methods to forecast palm oil exports and determine the most accurate method. The results show that the Trend Analysis method yields the lowest Mean Absolute Deviation (MAD = 18505.67) and Mean Squared Error (MSE = 436747200), indicating superior accuracy compared to the other methods. The findings suggest that Trend Analysis can provide stakeholders government, companies, and farmers with valuable insights for strategic decision-making. This research contributes to the development of more precise forecasting models, supporting Indonesia's palm oil industry in maintaining its global competitiveness.
subject: Articles
  topic: section
subject: Prediction
  topic: keywords
subject: , Palm Oil
  topic: keywords
subject: Moving Average
  topic: keywords
subject: Exponential Smoothing
  topic: keywords
subject: Trend Analysis
  topic: keywords
creator: Taufik Baidawi
creator: Muhammad Ridwan Effendi
creator: Heri Kuswara
creator: Siti Wardah
creator: Kursehi Falgenti
rights: Copyright (c) 2025 Taufik Baidawi, Muhammad Ridwan Effendi, Heri Kuswara, Siti Wardah, Kursehi Falgenti https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 60
ending page: 69
doi: 10.31294/jtk.v11i1.12115

Quality Of SMK E Rapor System Version 7.0.0 Using Webqual 4.0 Method At SMKS Manggala Tangerang City
type: item
description: SMK Manggala Tangerang City is one of the schools that implements an independent curriculum and has implemented the e-Rapor application to process grades and print student report cards since the odd semester of the 2022/2023 Academic Year until now. The eRapor application has been implemented at SMK Manggala and is accessed by all teachers, homeroom teachers, and administrators both online and offline. Currently, the eRapor Application used by SMK Manggala is an eRapor Application with Version 7.0.0. One of the innovations launched by the Directorate of Vocational High Schools, Ministry of Education, Culture, Research and Technology to support the Independent Curriculum is eReport.
subject: Articles
  topic: section
subject: eReport Vocational High School
  topic: keywords
subject: Webqual 4.00
  topic: keywords
subject: Independent Curriculum
  topic: keywords
creator: Nurmalasari Nurmalasari
creator: Titin kristiana
rights: Copyright (c) 2025 Nurmalasari Nurmalasari, Titin kristiana https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 53
ending page: 59
doi: 10.31294/jtk.v11i1.12114

Analysis of the Influence of System Quality, Information Quality, Service Quality on User Satisfaction and Continuance Use Intention in the Malang Mbois Application
type: item
description: The Malang Mbois application is designed to make it easier for the people of Malang City to access public information and service through one integrated application. Although it received a fairly good rating on the Play Store, some users reported problems, such as bugs, incomplete information, and data inaccuracies. The purpose of this study is to investigate the impact of system quality, information, and service on user satisfaction and long-term use of the Malang Mbois Application. This study employs the Delone and McLean models to assess system, information, and service quality, as well as the Expectation Confirmation Model to assess user satisfaction and intent to continue use. Data were collected from 81 application users using purposive sampling technique and analyzed using Stuctural Equation Modeling (SEM) method based on Partial Least Square (PLS) through SmartPLS software. Based on the results of data analysis that has been carried out using SmartPLS software, 4 hypotheses produce a path-coefficient value> 0.1, t-test value ≥ 1.96, p-value ≤ 0.05. The findings indicated that the quality of the system, the quality of information, and the services significantly affect user satisfaction. Additionally, user satisfaction strongly influences the intention of users to keep using the Malang Mbois application.
subject: Articles
  topic: section
subject: DeLone and McLean
  topic: keywords
subject: User Satisfaction
  topic: keywords
subject: Continuance Use Intention
  topic: keywords
subject: Aplikasi Malang Mbois
  topic: keywords
subject: Partial Least Square (PLS)
  topic: keywords
creator: Nilna Sa'idah
creator: Diema Hernyka Satyareni
creator: Ahmad Farhan
rights: Copyright (c) 2025 Nilna Sa'idah, Diema Hernyka Satyareni, Ahmad Farhan https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 41
ending page: 52
doi: 10.31294/jtk.v11i1.12113

Comparison of News Text Summarization Using NLTK and TextRank Based on Python Programming
type: item
description: Text summarization technology is increasingly used to simplify the vast amount of news information available in the digital era. This study compares two popular text summarization methods, the Natural Language Toolkit (NLTK) and TextRank, in the context of news summarization using the Python programming language. The goal of this research is to evaluate the performance of both algorithms based on summary quality and processing time. The dataset comprises a collection of news articles in Indonesian, processed using both methods. The results indicate that each algorithm has distinct advantages: TextRank excels in generating more coherent summaries, while NLTK demonstrates faster processing times. This study aims to contribute insights into the selection of an appropriate text summarization method for automating news summarization across various applications.
subject: Articles
  topic: section
subject: text summarization NLTK TextRank
  topic: keywords
subject: Python
  topic: keywords
subject: news
  topic: keywords
subject: text summarization
  topic: keywords
creator: Artika Surniandari
creator: Hilda Rachmi
creator: ahmad fauzi
creator: Lila Dini Utami
rights: Copyright (c) 2025 Artika Surniandari, Hilda Rachmi, ahmad fauzi, Lila Dini Utami https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 32
ending page: 40
doi: 10.31294/jtk.v11i1.12108

Development of an Android Application to Identify Fish Species Using Kotlin-based Android Studio
type: item
description: Indonesia has a rich variety of fish species, but the understanding and knowledge of the public about these fish species is still limited. This is the background of the problem raised in this research, with the aim of providing education to the Indonesian people about the types of fish in Indonesia. The solution offered is the development of an Android application that can identify fish species in Indonesia. This application is expected to be an effective educational tool for the community. The methods used in the development of this application include collecting data documentation from the internet, literature studies from various research journals, literature, and other scientific articles. The collected data is then used as a basis for application development. Tools used in the development of this application include Android Studio as the main software, Firebase as a database, Kotlin as a programming language, and XML as a markup language for data storage and transmission. Figma is used to design the application interface. Application testing is carried out using the black box testing method to ensure application functionality runs well. The algorithm used in this application is a recursive algorithm for processing fish identification data. With this application, it is hoped that Indonesian people can more easily recognize and understand the types of fish that exist in Indonesia, so that they can reduce the number of fish species in Indonesia
subject: Articles
  topic: section
subject: Android
  topic: keywords
subject: Kotlin
  topic: keywords
subject: App
  topic: keywords
subject: Fish Identidication
  topic: keywords
creator: Adam Mulya Sampurna
creator: Alfani Alfani
creator: Ade Setiawan
creator: Hanggoro Aji Al Kautsar
rights: Copyright (c) 2025 Adam Mulya Sampurna, Alfani Alfani, Ade Setiawan, Hanggoro Aji Al Kautsar https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 19
ending page: 31
doi: 10.31294/jtk.v11i1.12107

Blockchain's Impact on Coffee Supply Chains: A Systematic LiteratureReview
type: item
description: Coffee is a globally significant commodity with high economic value and extensive social impact. Nevertheless, the intricate nature of the coffee supply chain frequently leads to issues concerning visibility, traceability, and sustainability. Blockchain offers a decentralized, transparent, and secure approach to mitigate these problems effectively. This research seeks to explore the current trends, core motivations, and auxiliary technologies that facilitate the integration of blockchain technology within the coffee supply chain. A total of 16 scientific articles published in Scopus-indexed journals (Q1 to Q4) between 2019 and 2024 were selected and analyzed using the Systematic Literature Review (SLR) method. The findings reveal that traceability and transparency are the primary focuses of blockchain implementation. The study also identifies several supporting technologies, such as smart contracts, Distributed Ledger Technology, IoT, NFT, and Artificial Intelligence, which play critical roles in enhancing the efficiency and security of blockchain-based coffee supply chains.
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: Coffee
  topic: keywords
subject: Supply Chain
  topic: keywords
creator: Rina Rina
creator: Mulia Rahmayu
creator: Taopik Hidayat
rights: Copyright (c) 2025 Rina Rina, Mulia Rahmayu, Taopik Hidayat https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 11
ending page: 18
doi: 10.31294/jtk.v11i1.12105

Employee Payroll Accounting Application Using Extreme ProgrammingDevelopment Method
type: item
description: Technology allows company structures to become more effective and efficient for payroll system applications, as almost all parts of the company have adopted this technology. One such company, PT. Langgeng Engineering Tehnik Indonesia, which produces and sells spare parts, has an HR department responsible for managing extensive employee data, including payroll data. The process is currently done manually with the help of Microsoft Excel, requiring a high level of accuracy and being very prone to errors. The most significant issue is certainly payroll management, as it relates to the company's finances and employee welfare. To reduce the risk of errors in managing this data, particularly payroll, this research aims to contribute by creating a payroll application designed to facilitate management in processing and disbursing salaries to every employee working in the company, while also minimizing the risk of errors in management. The application is built using the Extreme Programming method, which offers advantages in terms of rapid development, efficiency, adaptability, and flexibility. This application is developed as a desktop-based solution using Java programming language, as its users are limited and it is intended for internal company use. The application is tested using Black Box testing, a technique that evaluates the functionality without considering the internal structure of components or systems. The testing is conducted with eight scenarios, and all tests yield valid results.
subject: Articles
  topic: section
subject: Payroll
  topic: keywords
subject: Accountancy
  topic: keywords
subject: Extreme Programming
  topic: keywords
subject: Employee
  topic: keywords
creator: Nafi Ramadhani
creator: Syarif Hidayatulloh
rights: Copyright (c) 2025 Nafi Ramadhani, Syarif Hidayatulloh https://creativecommons.org/licenses/by-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 1
ending page: 10
doi: 10.31294/jtk.v11i1.12103

WebQual 4.0 Analysis of Indonesian Election and Government Websites
type: item
description: The KPU is the body that organizes general elections in Indonesia. Its responsibilities include deciding which parties can contest elections, organizing voting and announcing the results and seats won in the various branches of government. Currently, many KPU website users in this case register as regional heads by visiting the KPU.go.id website. Problems that occur when visiting the KPU.go.id website include that when uploading files there is often a problem with the system response taking a long time to load, when logging in to move to a new tab the process takes too long. The quality of website services in terms of user perception and intensity of use of KPU website services is less than optimal. Based on the F Test results table, it is known that the significant value in the F test is 0.000, which is smaller than the determined significant value of 0.05, and the calculated F value of 61.883 is greater than 3.09. So it can be concluded that the variables usability (X1), information quality (X2) and interaction quality (X3) together (simultaneously) influence user satisfaction (Y).
subject: Articles
  topic: section
subject: KPU Website User Satisfaction Webqual 4.0 Website Quality
  topic: keywords
creator: Acmad Nurhadi
creator: Elly Indrayuni
rights: Copyright (c) 2026 Acmad Nurhadi, Elly Indrayuni https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 23
ending page: 30
doi: 10.31294/jtk.v12i1.11092

Intelligent Obstacle Detection And Warning System For Railroad Crossings Using Yolov8 And Bytetrack With Signal-Triggered Response
type: item
description: Railroad level crossings remain a critical safety concern due to the high incidence of collisions involving vehicles and pedestrians. This study proposes an intelligent, vision-based obstacle detection and warning system that integrates the YOLOv8 object detection model with the ByteTrack multi-object tracking algorithm. Designed for real-time operation on crossing surveillance video, the system can detect and track vehicles, pedestrians, and foreign objects within the railway zone. Consistent detection is coupled with a signal-triggered response mechanism, enabling early warnings to operators or automated gate control systems, particularly as trains approach. Evaluation on a diverse video dataset—captured under varying lighting and weather conditions demonstrates that the integration of YOLOv8 and ByteTrack improves tracking consistency, reduces false negatives, and maintains latency below 50 ms per frame. This research advances intelligent transportation safety systems and offers a vision-based solution that can be integrated into existing railway infrastructure.
subject: Articles
  topic: section
creator: Untung Rohwadi
creator: Indah Purwandani
creator: Rudianto
creator: Amrin
rights: Copyright (c) 2026 Untung Rohwadi ST, M.Kom, Indah Purwandani M.Kom, Rudianto M.Kom, Amrin S.Si, M.Kom https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 12
ending page: 22
doi: 10.31294/jtk.v12i1.11049

Design and Development of Mobile Based Laundry Business Application
type: item
description: With the rapid advancement of technology comes solutions to maintain the welfare of businesses, including the laundry industry, which relies on customer retention and efficient service to sustain business. This research outlines the development cycle of a mobile-based application prototype for laundry businesses, providing an approach to support ongoing operations and drive customer engagement forward. The study follows the Software Development Life Cycle (SDLC) methodology, incorporating Unified Modeling Language (UML) and the Waterfall model for a structured process in information systems development. The application prototype provides key features such as online ordering, real-time order tracking, digital payments, automated notifications, and customer feedback platform. Additionally, the application includes an integrated database management system to aid business owners in data processing, improving productivity and service efficiency. The interface is designed to be intuitive, ensuring accessibility for a diverse range of users. Testing results indicate that the application meets functional requirements and enhances customer and worker experience by automating work activities therefore reducing manual workload. The findings suggest that implementing digital solutions in laundry businesses can improve customer satisfaction, increase customer retention, and optimize business resources. This research contributes to the digital transformation of businesses in the laundry sector, demonstrating the importance of technology in service-based industries.
subject: Articles
  topic: section
subject: Laundry Business
  topic: keywords
subject: Software Development
  topic: keywords
subject: Application
  topic: keywords
subject: Mobile-Based Application
  topic: keywords
subject: Unified Modelling Languages (UML)
  topic: keywords
creator: Bhustomy Hakim
creator: Regina Hillary
creator: Carin Caroline
creator: Nadia Aurelia Clarissa
creator: Nathasia Oktarina Riyani
creator: Ria Pratama
rights: Copyright (c) 2026 Bhustomy Hakim, Regina Hillary, Carin Caroline, Nadia Aurellia Clarissa, Nathasia Oktarina Riyani, Ria Pratama https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 1
ending page: 11
doi: 10.31294/jtk.v12i1.10163