Indonesian Journal on Software Engineering (IJSE)
Indonesian Journal on Software Engineering (IJSE) is a journal in the field of Computer Science and Informatics published by the Research and Community Service Institute (LPPM) of Bina Sarana Informatika University and has an ISSN both in print (2461-0690) and online version (2714-9935). This journal contains scientific research results that focus on: Expert Systems, Information Systems, Web Programming, Mobile Programming, Games Programming, Data Mining, and Decision Support Systems. Publish Frequency: 2 times a year (June and December) Indonesian Journal on Software Engineering (IJSE) is a national scientific journal as a reference source for academics which contains the results of scientific research related to the field of computers and informatics. IJSE is accredited Sinta 4 by the Ministry of Research, Technology and Higher Education of the Republic of Indonesia Number.225/E/KPT/2022 Valid until the publication of Volume 12 Number 2 Of 2026.
LPPM Universitas Bina Sarana Informatika
A DETERMINISTIC ONTOLOGY-DRIVEN FRAMEWORK FOR GOVERNANCE-AWARE AUTOMATED SOFTWARE REQUIREMENTS SPECIFICATION GENERATION
Automated generation of Software Requirements Specifications (SRS) remains a persistent challenge in requirements engineering, particularly in governance-intensive environments where specifications must simultaneously preserve structural completeness, integrate domain-specific governance constraints, maintain explicit traceability, and support reproducible generation behaviour. Existing ontology-based, template-based, and AI-based approaches address these requirements only in isolation. This paper proposes and evaluates a deterministic ontology-driven transformation framework that generates SRS artifacts from an OntoUML-derived conceptual model (M), a governance-oriented domain ontology (D), and a declarative governance rule set (R), formalised as S = F(M, D, R). The framework employs a six-layer transformation architecture combining exhaustive baseline structural mapping with governance-aware semantic enrichment through declarative rule-based activation over a selectively merged ontology. A Python prototype using OWLReady2 was implemented and evaluated across five Service Request Management datasets and five FIBO-derived Financial Compliance datasets, using four metrics: Structural Coverage Ratio (SCR), Governance Enrichment Ratio (GER), Traceability Coverage (TC), and Deterministic Reproducibility (DR). Results confirm SCR = 1.00 and TC = 1.00 across all ten datasets, GER progressing from 0.0000 in baseline configurations to 0.1250 and 0.1429 in the two domains respectively, and DR confirmed through ten independent executions per dataset. These findings demonstrate that ontology-driven deterministic transformation provides a viable, extensible, and governance-aware foundation for automated SRS generation, addressing limitations present in existing template-based, ontology-based, and AI-based approaches. Keywords: software requirements specification, ontology-driven requirements engineering, deterministic transformation, governance-aware generation, rule-based activation, FIBO REFERENCES [1] Aberkane, A.-J., Poels, G., & Vanden Broucke, S. (2021). Exploring automated GDPR-compliance in requirements engineering: A systematic mapping study. IEEE Access, 9, 66542–66556. https://doi.org/10.1109/ACCESS.2021.3076921 [2] Alrumaih, H., Mirza, A., & Alsalamah, H. (2020). Domain ontology for requirements classification in requirements engineering context. IEEE Access, 8, 110300–110312. https://doi.org/10.1109/ACCESS.2020.2993838 [3] Alsanad, A. A., Chikh, A., & Mirza, A. (2019). A domain ontology for software requirements change management in global software development environment. IEEE Access, 7, 49352–49361. https://doi.org/10.1109/ACCESS.2019.2909839 [4] Beg, A., O’Donoghue, D., & Monahan, R. (2025). Formalising software requirements with large language models. arXiv. https://doi.org/10.48550/arXiv.2506.10704 [5] Darif, I., Politowski, C., El Boussaidi, G., Benzarti, I., & Kpodjedo, S. (2023). A model-driven and template-based approach for requirements specification. In Proceedings of the 26th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE. https://doi.org/10.1109/MODELS58315.2023.00018 [6] EDM Council. (2026). Financial Industry Business Ontology (FIBO): Financial compliance module. https://spec.edmcouncil.org/fibo/ [7] Fariha, A., Alwidian, S., & Azim, A. (2024). A systematic literature review on requirements engineering and maintenance for embedded software. IEEE Access, 12, 114263–114276. https://doi.org/10.1109/ACCESS.2024.3443271 [8] Gillani, M., Niaz, H. A., & Ullah, A. (2022). Integration of software architecture in requirements elicitation for rapid software development. IEEE Access, 10, 56158–56173. https://doi.org/10.1109/ACCESS.2022.3177659 [9] Gupta, A., Poels, G., & Bera, P. (2022). Using conceptual models in agile software development: A possible solution to requirements engineering challenges in agile projects. IEEE Access, 10, 128716–128742. https://doi.org/10.1109/ACCESS.2022.3221428 [10] Khan, R. A., Khan, S. U., Khan, H. U., & Ilyas, M. (2021). Systematic mapping study on security approaches in secure software engineering. IEEE Access, 9, 19139–19160. https://doi.org/10.1109/ACCESS.2021.3052311 [11] Korn, A., Gorsch, S., & Vogelsang, A. (2025). LLMREI: Automating requirements elicitation interviews with LLMs. arXiv. https://arxiv.org/abs/2507.02564 [12] Krishna, M., Gaur, B., Verma, A., & Jalote, P. (2024). Using LLMs in software requirements specifications: An empirical evaluation. arXiv. https://doi.org/10.48550/arXiv.2404.17842 [13] Mokos, K., Nestoridis, T., Katsaros, P., & Bassiliades, N. (2022). Semantic modeling and analysis of natural language system requirements. IEEE Access, 10, 84094–84109. https://doi.org/10.1109/ACCESS.2022.3197281 [14] Mosquera, D., Ruiz, M., & Pastor, O. (2025). Ontology-based NLP tool for tracing software requirements and conceptual models: An empirical study. Requirements Engineering, 30, 341–369. https://doi.org/10.1007/s00766-025-00447-4 [15] Quattrocchi, G., Pasquale, L., Spoletini, P., & Baresi, L. (2025). Can LLMs generate user stories and assess their quality? IEEE Transactions on Software Engineering. Advance online publication. [16] Rodrigues da Silva, A. (2021). Linguistic patterns, styles, and guidelines for writing requirements specifications: Focus on use cases and scenarios. IEEE Access, 9, 143506–143530. https://doi.org/10.1109/ACCESS.2021.3120004 [17] Schulze, S., & Pretorius, L. (2013). An exploratory case study on the requirements business processes of a typical South African high technology systems engineering company. SAIEE Africa Research Journal, 104(1), 11–21. [18] Shafikuzzaman, M., Islam, M. R., Zaman, S., Ma, A., & Sifat, A. I. (2025). On the effectiveness of zero-shot and few-shot pretrained language models for software requirement classification. IEEE Access, 13, 159439–159448. https://doi.org/10.1109/ACCESS.2025.3607813 [19] Voria, G., Casillo, F., Gravino, C., Catolino, G., & Palomba, F. (2025). RECOVER: Toward requirements generation from stakeholders’ conversations. IEEE Transactions on Software Engineering. Advance online publication.
software requirements specification, ontology-driven requirements engineering, deterministic transformation, governance-aware generation, rule-based activation, FIBO
Copyright (c) 2026 Indonesian Journal on Software Engineering (IJSE)
DESAIN UI/UX SISTEM INFORMASI MONITORING TUGAS SISWA MENGGUNAKAN METODE DESIGN THINKING
Guru sering mengalami kesulitan dalam memantau penyelesaian tugas siswa sehingga proses monitoring dan evaluasi pembelajaran belum berjalan secara optimal. Oleh karena itu, diperlukan sebuah sistem informasi yang mampu mempermudah pengelolaan tugas siswa secara digital. Penelitian ini bertujuan merancang desain User Interface (UI) dan User Experience (UX) Sistem Informasi Monitoring Tugas Siswa menggunakan metode Design Thinking yang meliputi tahapan Empathize, Define, Ideate, Prototype, dan Test. Pada tahap Ideate, dilakukan proses brainstorming untuk menghasilkan alternatif solusi yang divisualisasikan dalam bentuk Low Fidelity Wireframe sebelum dikembangkan menjadi prototype. Selanjutnya, prototype dievaluasi menggunakan metode System Usability Scale (SUS) dengan melibatkan 20 responden yang terdiri atas guru dan siswa. Hasil evaluasi menunjukkan nilai rata-rata SUS sebesar 81,5, yang termasuk kategori Excellent (Grade A) dan Acceptable, sehingga menunjukkan bahwa desain UI/UX yang dirancang memiliki tingkat usability yang tinggi dan dapat diterima dengan baik oleh pengguna. Hasil penelitian ini diharapkan dapat menjadi acuan dalam pengembangan sistem informasi monitoring tugas siswa yang lebih efektif, mudah digunakan, dan berorientasi pada kebutuhan pengguna. Kata Kunci: UI/UX; User Interface; User Experience; System Usability Scale; Design Thinking.
UI/UX; Sistem Informasi Monitoring; Design Thinking
PENGEMBANGAN SISTEM INFORMASI TERINTEGRASI UNTUK MANAJEMEN EVENT DAN AGENDA BUDAYA BERBASIS WEB
Pengelolaan informasi kegiatan budaya memiliki peran penting dalam mendukung pelestarian budaya, meningkatkan partisipasi masyarakat, serta mendorong perkembangan sektor pariwisata. Namun, Dinas Pariwisata dan Kebudayaan Kabupaten Majalengka masih mengelola data kegiatan budaya, agenda kegiatan, dan dokumentasi secara terpisah sehingga proses pengelolaan dan penyebaran informasi belum berjalan secara optimal. Tujuan dari penelitian studi ini untuk mengembangkan aplikasi berbasis web. Untuk mengelola acara dan agenda budaya sistem manajemen informasi yang mengintegrasikan kegiatan budaya, persiapan agenda, dokumentasi, dan publikasi informasi ke dalam satu platform. Sistem dikembangkan dengan metode Research and Development (R&D) menggunakan model Waterfall, yang terdiri dari beberapa tahap yaitu analisis kebutuhan, perancangan, implementasi, dan pengujian. Pengamatan, wawancara, dan studi sastra studi digunakan untuk mengumpulkan data dalam penelitian ini. Kotak pengujian blackbox, validasi pengguna, dan model kualitas McCall digunakan dalam evaluasi sistem. Temuan evaluasi menunjukkan bahwa evaluasi setiap fungsi sistem berada di dalam persyaratan yang telah ditetapkan, termasuk kegiatan penganggaran, penyusunan agenda, penyusunan dokumen, dan penyebaran informasi. Hasil pengecekan menunjukkan bahwa sistem berhasil membuat proses administrasi menjadi lebih efisien, memudahkan dalam mengelola informasi, serta membuka akses yang lebih luas bagi masyarakat terhadap informasi budaya. Sehingga, sistem yang dibuat dapat menjadi alat digital yang efektif dalam membantu kegiatan pengelolaan budaya serta menyebarkan informasi budaya secara berkelanjutan di Kabupaten Majalengka. Kata Kunci: sistem informasi, agenda budaya, manajemen event, aplikasi berbasis web, waterfall, McCall
INTEGRASI YOLOv8 DAN AST-DSRA UNTUK DETEKSI DAN PEMODELAN PENYEBARAN GANODERMA KELAPA SAWIT
Basal Stem Rot (BSR) caused by Ganoderma boninense remains a major constraint in oil palm plantations due to its soil-borne nature, latent infection phase, and spatial transmission through root system interactions. Although deep learning-based approaches have demonstrated promising performance in detecting Ganoderma from UAV imagery, most existing studies focus primarily on visual classification without considering disease spread dynamics. This study proposes a threshold-validated digital twin approach by integrating YOLOv8 with the Adaptive Spatial-Temporal Disease Spread Risk Assessment (AST-DSRA) framework to detect infected palms while simultaneously modeling spatial transmission risk. An RGB image dataset was independently acquired using a UAV at an altitude of 30 m above the canopy level in an oil palm plantation located in Kubu Raya Regency, West Kalimantan, Indonesia. The dataset was split into training and testing sets at an 80%:20% ratio. YOLOv8 was employed to classify healthy and Ganoderma-infected palms, with model evaluation yielding precision of 89.9%, recall of 90.5%, F1-score of 90.2%, and mAP of 91.4%. AST-DSRA modeled neighborhood-level transmission risk based on inter-tree proximity using a 10 m distance threshold aligned with standard plantation spacing. The results indicate that this integration enables the identification of spatial infection hotspots that cannot be inferred from visual detection alone. The main contribution of this study is the first integration of YOLOv8-based Ganoderma detection and AST-DSRA-based spatial spread analysis within an interpretable and operational digital twin framework for large-scale oil palm disease monitoring. Keywords: basal stem rot, Ganoderma boninense, oil palm, spatial disease spread, YOLOv8 REFERENSI [1] Anwari, A. C. 2024. Klasifikasi penyakit tanaman tomat melalui citra daun menggunakan metode convolutional neural network. Jurnal Ilmu Multidisiplin Universitas, 2(3), 639-647. https://doi.org/10.70294/jimu.v2i03.421 [2] Atika, E. P., & Maulana, A. S. 2025. Classification of oil palm leaf diseases using YOLOv8-Nano algorithm. Internet of Things and Artificial Intelligence Journal, 5(3), 603-611. https://doi.org/10.31763/iota.v5i3.988 [3] Escriba-Gelonch, M., Liang, S., & Hessel, V. 2024. Digital twins in agriculture: Orchestration and applications. Journal of Agricultural and Food Chemistry, 72(19), 10737-10752. https://doi.org/10.1021/acs.jafc.4c01934 [4] Evizal, R., & Prasmatiwi, F. E. 2022. Penyakit busuk pangkal batang dan performa produktivitas kelapa sawit. Jurnal Agrotropika, 21(1), 47. https://doi.org/10.23960/ja.v21i1.5617 [5] Flood, J., Bridge, P. D., & Pilotti, C. A. 2022. Basal stem rot of oil palm revisited. Annals of Applied Biology, 181, 160-181. https://doi.org/10.1111/aab.12772 [6] Hutagaol, P., Wirianata, H., & Kristalisasi, E. N. 2024. Perkembangan penyakit busuk batang pada kelapa sawit pasca peremajaan tanaman. Jurnal Agro Industri Perkebunan, 12(1), 41-50. https://doi.org/10.25181/jaip.v12i1.3272 [7] Hwang, S. F., Strelkov, S. E., Feng, J., Gossen, B. D., Howard, R. J., & Turnbull, G. D. 2011. Influence of cultivar resistance and inoculum density on root hair infection of canola (Brassica napus) by Plasmodiophora brassicae. Plant Pathology, 60(5), 820-829. https://doi.org/10.1111/j.1365-3059.2011.02457.x [8] Iliuta, M. E., Pop, E., Caramihai, S. I., & Moisescu, M. A. 2024. Digital twin - A review of the evolution from concept to technology and its analytical perspectives on applications in various fields. Applied Sciences, 14(13), 5454. https://doi.org/10.3390/app14135454 [9] Lestandy, M., & Nugraha, A. 2025. UAV image classification of oil palm plants using CNN ensemble model. Journal of Applied Informatics, 9(4), 1312-1318. [10] Mulya, Y., & Siregar, R. 2024. Rancang bangun sistem pendeteksian hama dan penyakit pada tanaman sawit dengan sensor IoT. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 11(2), 287-296. https://doi.org/10.25126/jtiik.20241128561 [11] Mulyatni, A. S., Pratiwi, D., Sasmita, M. A., Darmono, T. W., & Purwantara, A. 2022. Potensi electronic nose untuk mendeteksi penyakit busuk pangkal batang pada kelapa sawit. E-Journal Menara Perkebunan, 90(1). https://doi.org/10.22302/iribb.jur.mp.v90i1.472 [12] Sanchez-Vega, J. A., Torres-Madronero, M. C., Lopez-Morales, J. A., & Restrepo-Giron, A. D. 2025. Automatic detection of Ceroxylon palms by deep learning in a protected area in Amazonas (NW Peru). Forests, 16(7), 1061. https://doi.org/10.3390/f16071061 [13] Shehu, H. A., Uyaver, S., & Rasheed, J. 2025. YOLO for early detection and management of Tuta absoluta-induced tomato leaf diseases. Frontiers in Plant Science, 16, 1524630. https://doi.org/10.3389/fpls.2025.1524630 [14] Zakaria, L. 2023. Basal stem rot of oil palm: The pathogen, disease incidence, and control methods. In S. Sundram & A. Idris (Eds.), Ganoderma Diseases of Perennial Crops: Biology, Detection and Management (pp. 603-615). CABI Publishing. https://doi.org/10.1079/9781800621596.0036 [15] Zhang, J., Li, Y., Wang, Q., Liu, H., Zhang, J., & Wang, Y. 2024. HR-YOLOv8: A crop growth status object detection method based on YOLOv8. Electronics, 13(9), 1620. https://doi.org/10.3390/electronics13091620 [16] Zhou, Q., Li, Y., Gu, J., & Shao, J. 2024. Efficient optimized YOLOv8 model with extended vision. Sensors, 24(20), 6506. https://doi.org/10.3390/s24206506
ANALISIS POLA PRODUKSI SAMPAH MENGGUNAKAN K-MEANS, DBSCAN, DAN HIERARCHICAL CLUSTERING
The increasing volume of waste without data-driven management creates challenges in designing effective environmental policies. This study aims to identify waste production patterns using clustering approaches through three algorithms: K-Means, DBSCAN, and Hierarchical Clustering. The dataset consists of waste volume, population density, and collection frequency variables that were normalized prior to analysis. K-Means was applied for centroid-based segmentation, DBSCAN for density-based clustering and anomaly detection, and Hierarchical Clustering for analyzing structural relationships among regions. The results indicate that all three methods successfully grouped regions based on waste production characteristics using complementary approaches. K-Means produced stable segmentation, DBSCAN detected extreme-pattern regions, and Hierarchical Clustering provided hierarchical cluster structures. This research contributes to data-driven waste pattern mapping to support more effective and sustainable environmental decision-making. Keywords: Waste Production Patterns, Clustering Analysis, K-Means Algorithm, DBSCAN Algorithm, Hierarchical Clustering
INTERPRETABILITAS KLASIFIKASI TEKS KESEHATAN MENTAL BERBASIS TRANSFORMER MENGGUNAKAN LIME DAN KNOWLEDGE GRAPH
Perkembangan model berbasis Transformer telah meningkatkan akurasi klasifikasi teks kesehatan mental secara signifikan, namun interpretabilitas model masih menjadi tantangan utama karena sifatnya yang black-box. Meskipun berbagai pendekatan XAI (Explainable Artificial Intelligence) telah dikembangkan, integrasi visualisasi pengetahuan konseptual untuk mendukung interpretasi hasil prediksi masih terbatas. Penelitian ini bertujuan mengembangkan kerangka post-hoc explainability dengan mengombinasikan metode LIME dan visualisasi Knowledge Graph untuk meningkatkan transparasi klasifikasi teks Kesehatan mental berbasis Transformer. Empat model Transformer yaitu BERT, RoBERTa, DistilBERT, dan XLNet, dievaluasi menggunakan metrics Accuracy, Precision, Recall, F1-Score, dan AUC. Hasil eksperimen menunjukan bahwa DistilBERT memberikan performa terbaik dengan akurasi 88.91%, precision 88.94%, recall 88.91%, F1-score 88.90%, dan AUC sebesar 92.60%. Pendekatan LIME digunakan untuk mengidentifikasi kontribusi fitur secara lokal terhadap prediksi, sementara Knowledge Graph memvisualisasikan hubungan semantik antar entitas yang berpengaruh dalam proses klasifikasi. Hasil visualisasi menunjukan bahwa fitur linguistik dengan muatan emosional tinggi memiliki peran dominan dalam penentuan kategori. Penelitian ini berkontribusi dengan menyediakan kerangka interpretabilitas berbasis visual yang mendukung transparasi model. Pendekatan ini berpotensi mendukung pengembangan sistem deteksi kesehatan mental berbasis AI yang lebih transparan, dapat dipercaya, dan berorientasi pada prinsip human-centered AI. Kata Kunci: Explainable AI, Knowledge Graph, Kesehatan Mental, LIME, Model Transformer
SISTEM INFORMASI KOORDINASI KEGIATAN PADA KEMENTERIAN KOORDINATOR BIDANG PEMBANGUNAN MANUSIA DAN KEBUDAYAAN
Sistem informasi yang efektif sangat penting untuk meningkatkan koordinasi dan efisiensi dalam pengelolaan kegiatan pegawai di instansi pemerintah. Penelitian ini mengembangkan sistem informasi berbasis website untuk mendukung koordinasi kegiatan pegawai di Deputi V Kementerian Koordinator Bidang Pembangunan Manusia dan Kebudayaan (Kemenko PMK). Sistem ini dirancang menggunakan metode waterfall, yang merupakan pendekatan terstruktur dalam pengembangan perangkat lunak yang meliputi tahapan analisis, desain, implementasi, pengujian, dan pemeliharaan. Tujuan dari sistem ini adalah untuk menyederhanakan proses pengelolaan jadwal, memfasilitasi komunikasi antar pegawai, serta meningkatkan transparansi dan akuntabilitas dalam pelaporan kegiatan. Sistem ini diharapkan dapat mengurangi kendala koordinasi yang ada, meningkatkan efisiensi operasional, dan mendukung pelaksanaan tugas-tugas dengan lebih baik di lingkungan Kemenko PMK. Hasil dari implementasi sistem menunjukkan bahwa pendekatan waterfall memberikan struktur yang jelas dan terukur dalam proses pengembangan, sehingga menghasilkan sistem yang stabil dan sesuai dengan kebutuhan pengguna. Penelitian ini diharapkan memberikan kontribusi signifikan dalam praktik pengelolaan informasi di lingkungan pemerintahan serta memberikan dasar untuk pengembangan sistem serupa di instansi lain. Kata kunci: Penjadwalan, Laravel, Website, Sistem Informasi, dan Waterfall
IMPLEMENTASI SEO BERBASIS CONTENT MARKETING UNTUK MENINGKATKAN TRAFIK ORGANIK WEBSITE
Abstrak Industri perjalanan haji dan umrah di Indonesia mengalami pertumbuhan yang pesat, namun banyak pelaku usaha masih menghadapi tantangan dalam meningkatkan visibilitas digital. Penelitian ini menerapkan strategi SEO terintegrasi pada situs PT Hasanain Nusa Wisata yang sebelumnya hanya memperoleh rata-rata 42 kunjungan organik per hari tanpa satu pun kata kunci target di halaman pertama Google. Dengan menggunakan pendekatan action research selama empat bulan (Juli–Oktober 2025), strategi yang diterapkan mencakup SEO On-Page melalui publikasi 92 artikel berkualitas tinggi, optimasi internal linking yang sistematis, serta peningkatan aspek teknis halaman, dan SEO Off-Page melalui penempatan konten pada 10 situs eksternal dengan Domain Authority antara 10–60. Pemantauan dilakukan secara longitudinal menggunakan Google Search Console dan Google Analytics 4 terhadap 15 metrik utama. Hasilnya menunjukkan peningkatan signifikan di berbagai aspek: trafik organik naik 1.140% (dari 10 menjadi 124 sesi per hari), 13 dari 15 kata kunci target berhasil masuk halaman pertama hingga kedua Google, CTR meningkat 2,5 kali lipat (dari 2,1% menjadi 5,3%), bounce rate turun 36% (dari 67% menjadi 43%), durasi sesi meningkat 150% (dari 1 menit 23 detik menjadi 3 menit 28 detik), dan conversion rate mencapai 2,8%. Selain itu, kecepatan halaman desktop meningkat 76% (dari 8,7 detik menjadi 2,1 detik), jumlah backlink bertambah dari 0 menjadi 20, serta referral traffic naik 146%. Temuan ini membuktikan bahwa strategi SEO berbasis content marketing yang konsisten, terukur, dan terintegrasi mampu secara efektif meningkatkan visibilitas digital dan kinerja organik pada industri perjalanan haji dan umrah, di mana sinergi antara SEO On-Page dan Off-Page dengan publikasi harian menciptakan efek pengganda terhadap performa situs secara keseluruhan. Kata Kunci: Search Engine Optimization, Content Marketing, Trafik Organik, Digital Marketing, SEO Abstract The hajj and umrah travel industry in Indonesia has experienced rapid growth, yet many businesses still face challenges in improving their digital visibility. This study implemented an integrated SEO strategy on the PT Hasanain Nusa Wisata website, which initially received an average of only 42 organic visits per day and had no target keywords appearing on the first page of Google. Using an action research approach over a four-month period (July to October 2025), the study applied a twofold strategy: On-Page SEO, involving the publication of 92 high-quality articles, systematic internal linking, and technical page optimization; and Off-Page SEO, through content placement on 10 external websites with Domain Authority scores ranging from 10 to 60. Longitudinal monitoring was conducted using Google Search Console and Google Analytics 4 across 15 key performance metrics. The results showed substantial improvements in all major indicators: organic traffic increased by 1,140 percent (from 10 to 124 sessions per day); 13 out of 15 target keywords reached the first or second page of Google; the click-through rate rose 2.5 times (from 2.1 percent to 5.3 percent); the bounce rate decreased by 36 percent (from 67 percent to 43 percent); the average session duration grew by 150 percent (from 1 minute 23 seconds to 3 minutes 28 seconds); and the conversion rate reached 2.8 percent. In addition, desktop page speed improved by 76 percent (from 8.7 seconds to 2.1 seconds), the number of backlinks increased from 0 to 20, and referral traffic rose by 146 percent. Overall, the findings indicate that a consistent, data-driven, and well-structured content marketing-based SEO strategy is highly effective in enhancing digital visibility and organic performance in the hajj and umrah travel industry, where the synergy between On-Page and Off-Page SEO combined with daily content publication creates a strong multiplier effect on overall website performance. Keywords: Search Engine Optimization, Content Marketing, Organic Traffic, Digital Marketing, SEO
Copyright (c) 2025 Indonesian Journal on Software Engineering (IJSE)
Deteksi Dini Penyakit Ginjal Kronis Dengan Menggunakan Algoritma Deep Learning
Penyakit Ginjal Kronis (PGK) merupakan salah satu masalah kesehatan serius dengan tingkat prevalensi yang terus meningkat di berbagai negara. Kondisi ini sering kali baru terdeteksi pada tahap lanjut, sehingga upaya deteksi dini sangat diperlukan untuk memperlambat perkembangan penyakit serta mengurangi risiko komplikasi. Penelitian ini mengkaji penerapan tiga pendekatan deep learning, yaitu Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), dan Convolutional Neural Network (CNN), dalam klasifikasi data pasien untuk identifikasi awal PGK. Hasil kajian dari berbagai penelitian mutakhir menunjukkan bahwa metode ANN mampu memberikan hasil yang stabil ketika dipadukan dengan teknik seleksi fitur, sedangkan LSTM memiliki keunggulan dalam mengolah data berurutan sehingga efektif memprediksi perkembangan penyakit. Di sisi lain, CNN terbukti dapat mengekstraksi pola kompleks pada data medis dan meningkatkan akurasi sistem. Pendekatan gabungan atau ensemble yang mengintegrasikan lebih dari satu arsitektur juga dilaporkan mampu menghasilkan performa prediksi yang lebih tinggi dibandingkan metode tunggal. Temuan ini menegaskan bahwa integrasi teknik deep learning dengan strategi optimasi model dapat memberikan dukungan yang signifikan dalam sistem deteksi dini berbasis data medis, sehingga berpotensi membantu tenaga medis dalam proses pengambilan keputusan klinis yang lebih cepat dan tepat.
Aplikasi Deteksi Dini Kesehatan Mental Menggunakan Metode Certainty Factor
Abstrak Kesehatan mental merupakan isu global yang memerlukan perhatian serius, terutama di kalangan mahasiswa dan masyarakat umum. Keterbatasan akses layanan profesional dan stigma sosial menjadi hambatan utama deteksi dini gangguan kejiwaan. Penelitian ini mengembangkan Aplikasi Mobile Deteksi Dini Gangguan Kejiwaan dengan metode Certainty Factor (CF) dan memvalidasi sistem menggunakan dataset mental health dari Kaggle. Model pengembangan menggunakan pendekatan Waterfall yang mencakup analisis kebutuhan, perancangan sistem, implementasi, pengembangan aplikasi menggunakan bahasa pemrograman Java dengan tools Basic4Android (B4A), serta pengujian dan evaluasi. Aplikasi dirancang dengan lima menu utama: Informasi, Mulai Tes, Riwayat, Konsultasi, dan Tentang. Dataset "Student Depression Dataset" dari Kaggle dengan 36,200+ records digunakan untuk validasi, mencakup variabel akademik, gaya hidup, dan faktor psikososial. Metode CF menghitung tingkat keyakinan diagnosis dengan mengombinasikan bobot pakar dan input pengguna melalui kuesioner dengan tiga pilihan jawaban (Tidak, Kadang, Sering). Sistem mampu mendiagnosis tujuh jenis gangguan kejiwaan: Skizofrenia, Gangguan Kecemasan, Depresi, Bipolar, OCD, BPD, dan PTSD. Hasil pengujian menunjukkan akurasi sistem 80.6%, dengan sensitivitas 68.4% dan spesifisitas 84.2%. Validasi dengan dataset Kaggle memperkuat reliabilitas sistem dalam mengidentifikasi faktor risiko. Penelitian ini berkontribusi pada pengembangan e-health untuk deteksi dini kesehatan mental yang accessible, user-friendly, dan evidence-based. Kata Kunci: Kesehatan Mental, Certainty Factor, Sistem Pakar, Aplikasi Mobile, Basic4Android Abstract Mental health is a global issue requiring serious attention, particularly among students and the general population. Limited access to professional services and social stigma are major barriers to early detection of mental disorders. This study develops a Mobile Application for Early Detection of Mental Disorders using the Certainty Factor (CF) method and validates the system using mental health datasets from Kaggle. The development model employs a Waterfall approach encompassing requirements analysis, system design, implementation, application development using Java programming language with Basic4Android (B4A) tools, as well as testing and evaluation. The application is designed with five main menus: Information, Start Test, History, Consultation, and About. The "Student Depression Dataset" from Kaggle with 36,200+ records is used for validation, covering academic variables, lifestyle, and psychosocial factors. The CF method calculates diagnostic confidence levels by combining expert weights and user input through a questionnaire with three response options (No, Sometimes, Often). The system is capable of diagnosing seven types of mental disorders: Schizophrenia, Anxiety Disorder, Depression, Bipolar, OCD, BPD, and PTSD. Testing results demonstrate a system accuracy of 80.6%, with sensitivity of 68.4% and specificity of 84.2%. Validation with the Kaggle dataset strengthens the system's reliability in identifying risk factors. This research contributes to the development of accessible, user-friendly, and evidence-based e-health for early detection of mental health. Keywords: Mental Health, Certainty Factor, Expert System, Mobile Application, Basic4Android