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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI International Journal of Innovation in Mechanical Engineering and Advanced Materials Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Jurnal Informatika Polinema (JIP) Journal of Elektronik Sistem InformasI Jurnal Pengabdian IPTEK
Didiek Trisatya
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Digitalisasi Pengembangan Sistem KKN Universitas Pancasakti Tegal Berbasis GIS Menggunakan Metode Prototyping Didiek Trisatya
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.6160

Abstract

Universitas Pancasakti Tegal is a private university in Central Java. Among them is the existence of an obstacle regarding the manual registration process for participants, not knowing the location points for administering KKN at universities for students, the absence of reporting of location points for the KKN village area for Lecturer Field Supervisors (LFS) and the KKN committee conducting the search the shortest way to find an important location, the location for administering the KKN, and filling out the KKN implementation schedule based on the location where it has not been digitalized so that the KKN implementation has not been managed effectively. Therefore, designing an Online KKN system is necessary to replace the manual system. By using the prototyping method, a prototyping system is produced that acts to connect developers with users to interact with each other in the continuity of information system development. The research results were obtained by applying API maps in the Online KKN system using Mapbox GL JS v2.11.0 to help access locations and applying markers as markers for a location as well as applying algorithms to solve the problem of finding the shortest route from the starting point to the endpoint using the Dijkstra algorithm.
Sistem Informasi Berbasis Web untuk Pendaftaran Kompetisi Internasional Didiek Trisatya; Priyo Haryoko; Gunawan Gunawan; Nur Tulus Ujianto
JESII: Journal of Elektronik Sistem InformasI Vol 3 No 2 (2025): Journal of Elektronik Sistem InformasI - JESII (DECEMBER)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v3i2.4409

Abstract

The management of international student competitions in higher education institutions often faces challenges related to fragmented registration processes, manual data handling, and inefficient submission management. These issues may lead to data inconsistency, administrative errors, and limited transparency for participants. This research aims to design and implement a web-based information system that integrates competition registration and work submission into a single platform. The study adopts a system development–oriented approach, including requirement analysis, system design, implementation, and functional testing. The proposed system supports an integrated registration and submission process for participants, role-based access for administrators, administrative verification, and real-time status monitoring. The implementation results indicate that the system operates according to functional requirements and improves administrative efficiency, data accuracy, and accessibility for users. By centralizing registration and submission processes, the system reduces redundancy and simplifies competition management workflows. This research provides a practical solution for managing international student competitions, with a case implementation at Universitas Pancasakti Tegal.
Improving Customer Churn Detection Through Balanced Ensemble Learning Didiek Trisatya; Priyo Haryoko
Jurnal Informatika Polinema Vol. 12 No. 3 (2026): Vol. 12 No. 3 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i3.9500

Abstract

Predicting customer churn represents a major challenge for telecommunication providers, driven by fierce market competition and frequent customer switching that can significantly threaten long-term revenue stability. Failure to accurately identify customers with high churn potential often leads to ineffective retention strategies. This study examines the effectiveness of integrating data balancing techniques with ensemble learning models to enhance churn prediction performance on imbalanced datasets. A quantitative experimental method is applied using a publicly available telecommunications dataset. The preprocessing phase focuses on handling incomplete records, transforming categorical attributes into numeric representations, and scaling feature values to improve data quality. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied exclusively to the training data. The study evaluates three classifiers, including Logistic Regression as a baseline and two ensemble methods, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). Model performance is examined using several evaluation metrics such as accuracy, precision, recall, F1-score, and the Area Under the ROC Curve (AUC). The results reveal that ensemble learning approaches outperform Logistic Regression, particularly with respect to recall and AUC performance. LightGBM achieves the best overall performance and demonstrates stable predictive capability across all evaluation measures. Feature importance analysis reveals that customer tenure and billing-related attributes, including monthly charges and total charges, are dominant factors influencing churn behavior. These results demonstrate that integrating data balancing techniques with ensemble learning methods offers a robust and effective solution for supporting proactive customer retention initiatives in the telecommunications sector.
Assistance in the Development of Personal Data Protection (PDP) Policies and Standard Operating Procedures (SOPs) for Digital Public Services Didiek Trisatya; Sesilia Putri Maryanto; Adam Supriyadi; Raihan Eka Sanjaya
Jurnal Pengabdian IPTEK Vol. 1 No. 1 (2026): February 2026
Publisher : Sah Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid digitalization of public services at the village level has significantly increased the volume and sensitivity of personal data managed by local public institutions. Village administrations are required to ensure that personal data processing activities comply with legal, ethical, and security principles to prevent misuse, data breaches, and declining public trust. However, many village governments still lack formal internal policies and standardized procedures to operationalize personal data protection regulations within daily digital service activities. This condition highlights the need for practical and structured interventions that support local officials in translating regulatory requirements into implementable governance instruments. This study aims to examine the effectiveness of a structured assistance-based approach in supporting village officials to develop a Personal Data Protection (PDP) Policy and Standard Operating Procedures (SOPs) for Digital Public Services. A descriptive qualitative method was employed, with the study conducted at the Mintaragen Village Office, Indonesia. The assistance-based approach was implemented through four stages: initial assessment of digital service workflows and data management practices, capacity building on personal data protection principles, collaborative drafting of PDP policy and SOP documents, and evaluation of assistance outcomes. Data were collected through focused group discussions, semi-structured interviews, and document analysis, and were analyzed using descriptive qualitative techniques. The results show that the assistance-based approach improved village officials’ understanding of personal data protection responsibilities and enabled the successful formulation of a PDP Policy and Digital Public Service SOPs that are aligned with legal requirements and local operational needs. The findings indicate that participatory assistance can effectively bridge the gap between regulatory frameworks and practical implementation. This study contributes a replicable assistance-based model for strengthening personal data governance and enhancing the security, accountability, and professionalism of village-level digital public services.
Pengenalan Tool AI  Untuk Penyusunan Perangkat Ajar Pada Guru SMK Nu Kramat Agus Wibowo; Muhammad Fahmi Mubarok Nahdli; Diajeng Tyas Purwa Hapsari; Gunawan Raharjo; Didiek Trisatya
Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Vol. 4 No. 2 (2026): Juni 2026 - Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ahsana.v4i2.434

Abstract

Artificial Intelligence (AI) merupakan teknologi yang semakin banyak dimanfaatkan dalam dunia pendidikan untuk meningkatkan efektivitas dan efisiensi penyusunan perangkat ajar. Namun, pemahaman guru mengenai penggunaan AI, khususnya Claude AI, masih perlu ditingkatkan agar dapat dimanfaatkan secara optimal dalam mendukung proses pembelajaran. Kegiatan In House Training ini bertujuan untuk meningkatkan pengetahuan dan keterampilan guru SMK NU Islamiyah Kramat dalam menggunakan Claude AI untuk menyusun perangkat ajar. Metode yang digunakan meliputi penyampaian materi, demonstrasi, praktik langsung, diskusi, serta evaluasi melalui pre-test dan post-test dengan melibatkan 42 guru. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta pada seluruh indikator pelatihan, terutama pada teknik prompting P-K-T yang mengalami peningkatan kategori “Sangat Paham” dari 0% menjadi 75%. Kegiatan ini membuktikan bahwa pelatihan Claude AI efektif dalam meningkatkan kompetensi guru untuk menghasilkan perangkat ajar yang lebih inovatif, efektif, dan efisien.
Benchmarking Nine SMOTE-Balanced Classifiers Including Artificial Neural Network for CNC Predictive Maintenance Didiek Trisatya; Priyo Haryoko
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 8 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v8i1.38645

Abstract

Unplanned equipment failure in CNC manufacturing causes significant economic losses, driving demand for effective predictive maintenance (PdM). A critical research gap persists: existing studies on the AI4I 2020 Predictive Maintenance Dataset apply isolated classifiers under inconsistent preprocessing pipelines, preventing fair algorithmic comparison. No prior study has benchmarked nine diverse classifier families under a unified pipeline integrating SMOTE oversampling with domain-driven feature engineering. This study addresses that gap by systematically evaluating nine ML classifiers—Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, SVM (RBF kernel), Naive Bayes, and MLP Neural Network—on the AI4I 2020 dataset (10,000 records; 3.4% failure rate; 1:28 class imbalance). Two domain-engineered features were constructed: mechanical power (P = n × T × (π/30)) and thermal gradient (ΔT = T_process - T_air). Features were normalized; SMOTE was applied to training folds only; and 10-fold stratified cross-validation assessed six performance metrics. Three novel contributions are presented: (1) the first nine-classifier benchmark on AI4I 2020 under a unified SMOTE-and-feature-engineering pipeline enabling fair model comparison; (2) empirical demonstration that Average Precision is a more discriminating evaluation metric than AUC-ROC under severe 1:28 class imbalance; and (3) physical interpretation of feature importance linking dominant predictors to CNC failure mechanisms. Gradient Boosting achieved the best-balanced performance (F1-score: 0.6782, Accuracy: 97.20%, AUC-ROC: 0.9723); Random Forest attained the highest AUC-ROC (0.9772). Mechanical power (25.51%) and tool wear (23.91%) were dominant predictors, corresponding to tribological, fatigue loading, and thermal failure mechanisms. These findings support cost-effective condition-based maintenance strategies in industrial CNC environments.
Co-Authors Adam Supriyadi Agus Wibowo Diajeng Tyas Purwa Hapsari Gunawan Gunawan Raharjo Muhammad Fahmi Mubarok Nahdli Nur Tulus Ujianto Priyo Haryoko Priyo Haryoko Raihan Eka Sanjaya Sesilia Putri Maryanto