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All Journal TEPIAN JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal Lentera Pengabdian
Rachmadani, Budi
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Information System for Services and Management of Fishermen's Data from the Kutai Kartanegara Marine and Fisheries Service Fendi, Fendi; Franz, Annafi; Rachmadani, Budi
TEPIAN Vol. 6 No. 1 (2025): March 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i1.1848

Abstract

This research generally discusses the service and data management in the Department of Marine Affairs and Fisheries who still store documents in hard files, This problem arises because the fisherman administration service at the Maritime Affairs and Fisheries Service is still using manual methods in managing data and data storage, causing data management and administrative services to take a slow time and sometimes the existing data is no longer accurate. From the needs of the community, they have a desire for these services to be fast, reliable, transparent, and trustworthy services. With the service information system and web-based data management, it makes it easier for the public and admins to access and find out fisherman data by accessing anywhere. The tool is used to describe the system model in the form of Entity Relationship Diagram (ERD). To implement the service information system and fisherman data management, supporting components are needed to work properly. The component uses Laravel framework programming and stores database in MySQL. The results of this research and application are expected to be able to overcome existing problems and be useful for those concerned.
Pelatihan Penerapan Artificial Intelligence (AI) untuk Menunjang Aktifitas Pembelajaran Pada Sekolah Dasar Daarul Hijrah Al-Amin Samarinda Franz, Annafi; Maria, Eny; Suswanto, Suswanto; Yulianto, Yulianto; Rachmadani, Budi; Junirianto, Eko; Nurhuda, Asep; Khamidah, Ida Maratul; Ramadhani, Suci; Muslimin, Muslimin; Beze, Husmul; Andrea, Reza; Karim, Syafei; Putra, Emil Riza; Ramadhani, Fajar; Satria, Bagus; Imron, Imron
Lentera Pengabdian Vol. 1 No. 04 (2023): Oktober 2023
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/lp.v1i04.139

Abstract

Dalam era perkembangan teknologi yang cepat dan globalisasi yang semakin luas, pendidikan di tingkat Sekolah Dasar (SD) menghadapi tantangan untuk terus meningkatkan kualitas pembelajaran. Kecerdasan Buatan (Artificial Intelligence atau AI) telah muncul sebagai sebuah alat potensial yang dapat merevolusi metode pembelajaran dan meningkatkan keterlibatan siswa. Namun, untuk berhasil menerapkan AI dalam kurikulum SD, para pendidik memerlukan pemahaman dan pelatihan yang memadai. Pengabdian ini bertujuan untuk memberikan latar belakang dan implementasi pelatihan penerapan AI dalam konteks pembelajaran di Sekolah Dasar Daarul Hijrah Al-Amin, Samarinda. Melibatkan para guru sebagai peserta utama, pelatihan ini berfokus pada memperkenalkan konsep dasar AI dan memberikan panduan praktis tentang cara mengintegrasikan teknologi ini ke dalam metode pembelajaran yang sudah ada. Harapannya, pelatihan ini akan membantu para pendidik dalam menciptakan lingkungan pembelajaran yang adaptif, interaktif, dan sesuai dengan kebutuhan individu siswa, dengan demikian, mendorong perkembangan keterampilan dan pemahaman yang lebih mendalam. Hasil dari pengabdian ini diharapkan dapat berkontribusi signifikan dalam pembaruan pendekatan pembelajaran di tingkat SD dan menciptakan dasar yang kokoh untuk peningkatan kualitas pendidikan dalam menghadapi tuntutan zaman modern yang terus berkembang.
Support Vector Machine for Classifying Prostate Cancer Data B, Muslimin; Rachmadani, Budi; Rudito, Rudito
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.205

Abstract

Prostate cancer is one of the most prevalent cancers among men worldwide, making early detection and accurate classification essential for improving patient outcomes. This study investigates the application of Support Vector Machine (SVM) models for classifying prostate cancer using clinical and demographic data. Features such as prostate-specific antigen (PSA) levels, Gleason scores, tumor stage, and patient age were utilized to train and evaluate the model. Comprehensive preprocessing techniques, including handling missing values, feature normalization, and addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE), were employed to ensure robust model performance. The SVM model, optimized with a radial basis function (RBF) kernel, achieved an accuracy of 94.2%, with precision, recall, and F1-scores indicating reliable classification of both cancerous and non-cancerous cases. However, the results highlight challenges with the minority class, emphasizing the need for better handling of imbalanced datasets. Explainability techniques such as SHAP (Shapley Additive Explanations) were integrated to provide interpretable insights into the model’s predictions, with PSA levels and Gleason scores identified as the most influential features. This research demonstrates the potential of SVM in prostate cancer classification, providing a foundation for integrating machine learning models into clinical workflows for improved diagnostic precision and patient care.
Co-Authors annafi franz, annafi Emil Riza Putra Fajar Ramadhani Fendi Fendi Husmul Beze, Husmul Ida Maratul Khamidah Imron Imron, Imron Junirianto, Eko Karim, Syafei Maria, Eny Muslimin B, Muslimin Muslimin Muslimin Nurhuda, Asep Reza Andrea Rudito Rudito Satria, Bagus Suci Ramadhani, Suci Suswanto Suswanto, Suswanto Yulianto Yulianto