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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) International Journal of Advances in Data and Information Systems Journal of Social Research Malcom: Indonesian Journal of Machine Learning and Computer Science Sewagati Jurnal Informatika Polinema (JIP)
Santoso, Bagus Jati
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Pelatihan Deployment Aplikasi Berbasis Website SMK Pawiyatan Surabaya Pratomo, Baskoro Adi; Zaini, Alfa Fakhrur Rizal; Teja, Andika Rahman; Prinandika, Arya Gading; Fadhila, Farah Dhia; Arsyad, Hammuda; Fikriansyah, Irsyad; Al Kanza, Kalyana Putri; Vinorian, Muhammad Ersya; Diani, Nabila A'idah; Pramudya, Rafli Raihan; Ahmad, Tohari; Santoso, Bagus Jati; Studiawan, Hudan; Shiddiqi, Ary Mazharuddin; Alzamzami, Moch. Nafkhan; Anggoro, Radityo; Djanali, Supeno; Ijtihadie, Royyana Muslim; Suadi, Wahyu
Sewagati Vol 8 No 5 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i5.2091

Abstract

Permintaan akan tenaga kerja terampil di bidang pengembangan perangkat lunak dan website terus meningkat seiring dengan pertumbuhan industri teknologi informasi. Di tengah situasi ini, banyak sekolah yang berupaya memenuhi kebutuhan tersebut dengan membuka program-program pendidikan khusus dalam pengembangan perangkat lunak dan website . Salah satu contohnya adalah SMK Pawiyatan Surabaya. Sebagai respons terhadap kondisi ini, kami berencana untuk menyelenggarakan program pelatihan kompetensi yang fokus pada deployment aplikasi berbasis website . Strategi pelaksanaan program ini dirancang secara terstruktur, dimulai dari pembelajaran konsep dasar hingga penerapan praktis deployment aplikasi secara langsung. Selain itu, program ini akan dilengkapi dengan evaluasi progres berkala guna memantau dan meningkatkan pemahaman serta keterampilan peserta sepanjang program berlangsung. Output yang diharapkan dari program ini termasuk laporan kegiatan yang komprehensif, dokumentasi video untuk memperlihatkan tahapan pelaksanaan program, serta berita acara yang mencatat semua aspek terkait program pelatihan ini. Dengan mengimplementasikan program pelatihan kompetensi deployment aplikasi berbasis website ini, SMK Pawiyatan Surabaya diharapkan dapat memberikan kontribusi yang signifikan dalam mempersiapkan generasi muda Indonesia untuk menjadi tenaga kerja yang kompeten dan siap beradaptasi dengan dinamika industri teknologi informasi global.
Analisis Sentimen Ulasan Pengguna Aplikasi Mobile SP4N-LAPOR! dengan Pendekatan Machine Learning Sulistiowati, Yeni; Santoso, Bagus Jati
Jurnal Informatika Polinema Vol. 11 No. 3 (2025): Vol. 11 No. 3 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v11i3.7189

Abstract

Kemajuan teknologi informasi di Indonesia telah mendorong peningkatan akses internet, yang kini mencapai 79,5% dari total populasi. Kondisi ini menciptakan peluang besar bagi digitalisasi layanan publik, termasuk penerapan Sistem Pengelolaan Pengaduan Pelayanan Publik Nasional (SP4N-LAPOR!). Aplikasi ini memungkinkan masyarakat untuk menyampaikan pengaduan dengan cepat dan transparan melalui berbagai platform, termasuk aplikasi mobile. Semakin populernya perangkat mobile semakin memperkuat peran SP4N-LAPOR! dalam mendorong partisipasi masyarakat dalam pengawasan pelayanan publik. Namun, sebagian besar penelitian sebelumnya masih terbatas pada analisis data kualitatif dan kuantitatif deskriptif, sehingga pengalaman serta perspektif pengguna sebagai elemen utama dalam aplikasi ini belum tergambar secara komprehensif. Untuk mengatasi keterbatasan tersebut, penelitian ini mengusulkan penggunaan analisis sentimen terhadap ulasan masyarakat yang diberikan melalui aplikasi mobile berbasis Android. Pendekatan ini bertujuan untuk mengidentifikasi permasalahan teknis serta memberikan gambaran mengenai kualitas layanan yang disediakan oleh penyelenggara. Analisis dilakukan menggunakan teknik Machine Learning dengan tiga model utama: Naive Bayes (NB), Support Vector Machine (SVM), dan kombinasi keduanya (NBSVM) dengan tambahan fitur ekspansi N-gram. Hasil pengujian menunjukkan bahwa model NBSVM memiliki performa terbaik dengan G-Mean sebesar 0.8451, Sensitivity sebesar 0.8227 dan F2 Score sebesar 0.8215, mengungguli model NB dan SVM. Hasil dari penelitian ini diharapkan dapat menjadi alat evaluasi yang efektif bagi penyelenggara pelayanan publik serta memperkuat peran pengawasan Ombudsman RI dalam pelaksanaan SP4N-LAPOR!.
Analysis of Helpdesk System Development in A Manufacturing Company using Design Thinking Approach Sofi, Khalis; Santoso, Bagus Jati
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1354

Abstract

The IT department is vital in manufacturing companies, including PT XYZ, where operations involve transforming raw materials into finished goods. With the complexity of these activities, IT support is essential for managing software and hardware that aligns with business processes. PT XYZ implemented a helpdesk system to streamline IT services but encountered communication issues in the ticketing feature, affecting system efficiency and effectiveness. This research aimed to improve communication between users and administrators to enhance efficiency in monitoring and maintaining IT infrastructure. The study used a design thinking approach, chosen for its collaborative, flexible, and adaptive nature. The process began with the empathize stage, using usability scales and in-depth interviews with users to identify pain points and gather insights. Define, ideate, and prototype stages involved brainstorming and designing solutions in collaboration with the IT team. Finally, the testing stage evaluated user feedback on the improved system. The redevelopment of the helpdesk system yielded significant results, including a 12.27-point increase in usability scale scores. Enhanced features addressed user needs effectively, and all components of the upgraded system were well-received during testing. The improvements led to more structured and systematic communication, making the helpdesk system at PT XYZ more effective and efficient.
A Tag-Constrained Top-k Shortest Path for Finding Diverse Routes Santoso, Bagus Jati; Tamtama Adi, Ibrahim; Ijtihadie, Royyana Muslim
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.95815

Abstract

The top-k shortest path problem is a fundamental topic in graph theory and pathfinding applications. Traditional approaches focus solely on finding k paths with the least total cost or distance, often resulting in highly similar paths that offer limited flexibility for user selection. Moreover, real-world navigation demands often involve additional user preferences, such as specific points of interest or required amenities along the route. Motivated by this observation, this paper proposes an efficient framework for answering top-k diverse path search queries incorporating user-specified tag preferences. Specifically, given a source and destination node, a set of user-defined tags, and a similarity threshold, our method retrieves k shortest paths that not only satisfy the user's tag constraints but also maintain diversity by ensuring that the similarity among the retrieved paths remains below a given threshold. The proposed solution employs a two-phase approach: (1) preprocessing the graph structure to generate a tag-based matrix and shortest path data for efficient query processing, and (2) a hybrid search strategy that combines a modified Dijkstra’s algorithm and depth-first search with pruning based on tag satisfaction and diversity checking. Extensive experiments on synthetic road network datasets demonstrate that our method achieves significant improvements in query processing efficiency and provides a higher degree of path diversity compared to conventional approaches. Our contributions include the formal definition of the top-k diverse path search with tag preferences, the development of an efficient search framework, and comprehensive experimental validations. The results suggest that the proposed framework effectively balances path optimality, tag satisfaction, and diversity, enabling a more flexible and user-centric pathfinding system.
Implementasi Feature Selection Menggunakan Boruta untuk Peningkatan Akurasi Model Lapser Prediction: Implementation of Feature Selection Using Boruta to Improve the Accuracy of the Lapser Prediction Model Saputra, Mochamad Gilang; Santoso, Bagus Jati
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1992

Abstract

Memprediksi pelanggan lapser menjadi tantangan utama di sektor layanan data yang kompetitif, disertai tingginya biaya akuisisi pelanggan baru. Penelitian ini mengusulkan pendekatan feature selection menggunakan Boruta untuk meningkatkan akurasi model lapser, dengan menerapkan teknik wrapper pada Random Forest. Proses modeling lapser prediction menggunakan algoritma machine learning Gradient Boosting yang dianalisis sebelum dan sesudah seleksi fitur Boruta. Hasil eksperimen pada data menunjukkan bahwa Boruta efektif dalam meningkatkan metrik utama (akurasi, recall, dan AUC). Model Gradient Boosting meraih akurasi hingga 75.10%, recall 74.42%, dan AUC 82.18% setelah menggunakan Boruta. Sebelum menggunakan Boruta nilai akurasi 71.74%, recall 68.74%, dan AUC hanya 77.77%. Temuan tersebut menegaskan bahwa pendekatan yang diusulkan dapat memprediksi lapser secara lebih dini, serta membantu penyusun kebijakan menyusun strategi retensi pelanggan yang lebih efektif, sehingga meminimalkan potensi kerugian dan memperkuat daya saing di pasar.
Machine Learning-Based Prediction of Divorce Verdicts Using Posita Data and Imbalanced Data Handling: A Case Study in Padang Sidempuan Rahmadini, Rina; Santoso, Bagus Jati
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1405

Abstract

This study aims to develop a predictive model for divorce verdicts ("Granted" or "Rejected") in the Religious Courts of Indonesia using machine learning techniques. The dataset consists of 2,026 finalized divorce cases from the Religious Court of Padang Sidempuan between 2018 and 2025, incorporating structured variables and posita—narrative texts describing the plaintiff’s reasons for divorce. Keyword-based feature extraction was applied to transform these texts into interpretable indicators. To handle class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was implemented on the training data. Six classical machine learning algorithms were evaluated: Decision Tree, Naïve Bayes, K-Nearest Neighbors, Random Forest, LightGBM, and XGBoost. Performance was measured using accuracy, precision, recall, F1-score, F2-score, and AUC. The results indicate that Naïve Bayes achieved the highest recall (100%) for the “Granted” class, while LightGBM and XGBoost demonstrated the most balanced performance across both classes. Feature importance analysis revealed that mediation outcomes, domestic violence, and economic hardship were among the most influential factors in determining verdicts. The study highlights the applicability of interpretable machine learning in legal decision support and discusses limitations such as the single-court scope and challenges in predicting minority class outcomes. Future work may explore multi-jurisdictional data, deep learning approaches, and domain-specific embeddings for enhanced performance.
Decentralized Electronic Health Record Management with Semantic-Aware Hierarchical Encryption Kurniyanto, Firdaus Putra; Santoso, Bagus Jati
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1413

Abstract

The rising incidence of cyberattacks targeting electronic health records (EHR) in Indonesia necessitates a robust and context-aware data protection scheme. This paper proposes a decentralised EHR management system that leverages blockchain, IPFS, and a novel Semantic-Aware Hierarchical Encryption (SAHE) algorithm. SAHE enables multi-level access control based on data sensitivity semantics, ensuring privacy while maintaining usability for medical professionals. The system was implemented in a prototype environment and evaluated through stress testing with up to 200 users, achieving an average CPU usage of 55% and a memory consumption of 80.2 MB. Differential cryptanalysis demonstrated a strong avalanche effect (~50%), with no vulnerabilities found via OWASP ZAP scanning. This architecture offers a promising solution for privacy-preserving, patient-controlled EHR systems, particularly in regions with limited infrastructure.
Analysis of the Effect of the Use of a Learning Management System on the Improvement of Human Resource Competence Using the " " Method, Partial Least Squares, and Structural Equation Modeling Mujahidin, Noor Qidam; Santoso, Bagus Jati
Journal of Social Research Vol. 4 No. 9 (2025): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v4i8.2728

Abstract

Digital transformation drives companies to continuously develop human resource (HR) competencies to remain relevant and competitive. One of the main challenges is ensuring that technology-based learning systems, such as Learning Management Systems (LMS), are truly effective in supporting employee competency development. This study aims to analyze the impact of the use of the TAMARA LMS at PT IDStar Cipta Teknologi on improving HR competencies and formulate improvement strategies based on the evaluation of the Technology Acceptance Model 2 (TAM2). The approach used is a mixed methods approach, with quantitative analysis using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) technique, and qualitative analysis through in-depth interviews. The results indicate that Perceived Ease of Use and Attitude Toward Using have a significant effect on Actual Usage, while several external constructs in TAM2, such as Job Relevance and Result Demonstrability, do not show a strong influence on Perceived Usefulness. Qualitative findings support the contribution of LMS to improving soft skills and digital skills, but also highlight challenges such as the lack of regular evaluation, limited content, and minimal managerial support. A synthesis of the two approaches concludes that the TAM2 model remains relevant but needs to be refined to suit the organizational context. This study provides strategic recommendations for optimizing the role of the LMS as an effective and sustainable HR development tool.
Analisis Ketidaktercapaian Key Performance Indicator Departemen Engineering PT. XYZ dalam Implementasi Computerized Maintenance Managemen System Winoto, Maria Nicolas Ganden Gondho; Santoso, Bagus Jati; Suef, Mokh.
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i5.15859

Abstract

Seiring dengan meningkatnya jumlah produk dan kapasitas, PT. XYZ, perusahaan material bangunan, memutuskan untuk melakukan transformasi digital, dimulai dari Departemen Engineering dengan mendigitalisasi proses perawatan dan perbaikan mesin-mesin produksi. Transformasi ini mencakup pelaporan kegiatan, perencanaan, dan ketersediaan suku cadang yang harus dapat diakses dengan mudah untuk memastikan efisiensi manajemen perawatan mesin. Pengembangan teknologi digital dilakukan secara internal antara Departemen Engineering dan Departemen Information Technology, menghasilkan aplikasi Computerized Maintenance Management System (CMMS) untuk meningkatkan capaian Key Performance Indicator (KPI) Departemen Engineering. Namun, penerapan aplikasi ini belum mencapai KPI secara konsisten, menimbulkan keraguan terhadap kegunaannya. Penelitian ini bertujuan untuk mengidentifikasi penyebab ketidaktercapaian KPI dengan menggunakan metode root cause analysis, framework for information system success, kuesioner, dan analisis kompetensi personil engineering. Hasil penelitian menunjukkan bahwa ketidaktercapaian KPI disebabkan oleh klasifikasi, distribusi, dan verifikasi downtime yang masih manual, rekapitulasi downtime yang tidak akurat, tidak adanya prioritas jenis mesin, serta data mesin dan item perawatan yang belum lengkap. Langkah perbaikan yang diusulkan meliputi penambahan sistem klasifikasi, distribusi, dan verifikasi downtime pada aplikasi, penambahan validator downtime, simplifikasi mesin dan peralatan, pembuatan sistem klasifikasi jenis surat perintah kerja (SPK), pembaruan data mesin, serta item perawatan dan jadwal maintenance secara konsisten. Perbaikan penerapan CMMS mencakup penggantian perangkat jaringan, standarisasi hak akses, penambahan sistem klasifikasi distribusi otomatis, melengkapi nomor aset, dan pelatihan operasional CMMS secara reguler. Kesimpulannya, implementasi perbaikan ini diharapkan dapat meningkatkan pencapaian KPI Departemen Engineering PT. XYZ secara konsisten.
Pembangunan Model Klasifikasi untuk Menilai Lokasi Strategis Usaha Laundry Berdasarkan Analisis Geospasial dan Data Open-Source: Development of a Classification Model to Evaluate Strategic Laundry Business Locations Using Geospatial and Open-Source Data Riznov, Briandana; Santoso, Bagus Jati
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2358

Abstract

Industri jasa cuci pakaian di Indonesia terus mengalami pertumbuhan pesat seiring perubahan gaya hidup masyarakat urban yang semakin praktis. Namun, banyak pelaku usaha mengalami kegagalan akibat pemilihan lokasi yang kurang tepat, sehingga diperlukan pendekatan berbasis data untuk mengevaluasi kelayakan lokasi usaha secara objektif. Penelitian ini bertujuan untuk mengembangkan model klasifikasi berbasis analisis geospasial dan data open-source guna menilai apakah lokasi usaha laundry eksisting di Surabaya tergolong strategis atau tidak strategis. Data penelitian dikumpulkan dari Google Maps API, OpenStreetMap (OSM), dan Badan Pusat Statistik (BPS), yang mencakup indikator spasial, demografis, dan ekonomi. Proses analisis meliputi pembersihan data, rekayasa fitur, penyeimbangan data dengan SMOTE, serta penerapan lima algoritma klasifikasi Logistic Regression, Decision Tree, Naive Bayes, k-Nearest Neighbor, dan Random Forest dengan evaluasi menggunakan metrik Accuracy, Precision, Recall, F1-score, dan ROC-AUC. Hasil penelitian menunjukkan bahwa algoritma Random Forest memberikan performa terbaik dengan akurasi 90,1% dan ROC-AUC 0,957. Faktor kepadatan penduduk, kedekatan dengan permukiman, dan jumlah kompetitor terbukti paling berpengaruh terhadap tingkat strategis lokasi. Penelitian ini berkontribusi dalam menyediakan kerangka evaluasi berbasis data dan geospasial yang dapat digunakan sebagai dasar pengambilan keputusan bisnis yang lebih objektif, terukur, dan adaptif terhadap dinamika perkotaan.
Co-Authors Al Kanza, Kalyana Putri Alzamzami, Moch. Nafkhan Arsyad, Hammuda Ary Mazharuddin Shiddiqi Baskoro Adi Pratomo Diani, Nabila A'idah Fadhila, Farah Dhia Fikriansyah, Irsyad Hudan Studiawan Khalis Sofi Kurniyanto, Firdaus Putra Mokh. Suef Mujahidin, Noor Qidam Pramudya, Rafli Raihan Prinandika, Arya Gading Radityo Anggoro, Radityo Rahmadini, Rina Riznov, Briandana Royyana Muslim Ijtihadie Saputra, Mochamad Gilang Sulistiowati, Yeni Supeno Djanali Tamtama Adi, Ibrahim Teja, Andika Rahman Tohari Ahmad Vinorian, Muhammad Ersya Wahyu Suadi Winoto, Maria Nicolas Ganden Gondho Zaini, Alfa Fakhrur Rizal