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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA Information Management For Educators And Professionals (IMBI) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) METIK JURNAL Building of Informatics, Technology and Science Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics Just TI (Jurnal Sains Terapan Teknologi Informasi) Brahmana : Jurnal Penerapan Kecerdasan Buatan Tematik : Jurnal Teknologi Informasi Komunikasi TEPIAN Buletin Poltanesa Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Informatika Wicida Jurnal Riset Inossa : Media Hasil Riset Pemerintahan, Ekonomi dan Sumber Daya Alam eProceedings of Engineering J-Icon : Jurnal Komputer dan Informatika Journal of Informatics, Electrical and Electronics Engineering Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Sasambo: Jurnal Abdimas (Journal of Community Service)
Pitrasacha Adytia
Sistem Informasi, STMIK Widya Cipta Dharma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Neuroscience Social Sciences Transportation Other

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Rancang Bangun Sistem Layanan Pengaduan Pusat Komputer Berbasis Website Pratama, Jerio Putra; Adytia, Pitrasacha; Harianto, Kusno
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 2 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i2.2823

Abstract

This study aims to design and develop a web-based Puskom Complaint Service System that facilitates integrated and transparent complaint reporting, handling, and monitoring processes. The system development method used is the Software Development Life Cycle (SDLC) Waterfall model, which consists of the stages of requirements analysis, system design, implementation, testing, and maintenance. The system is developed using the PHP programming language with the Laravel framework, MySQL as the database, and Vue.js as the front-end technology to provide a modern user interface. The result of this study is a web-based system that has been successfully implemented with key features, including registration and login for students and staff, an online complaint submission form, online complaint status updates (open, progress, resolved, rejected), and a monitoring dashboard. Functional testing using the Black Box Testing method shows that 10 test scenarios were successfully executed with a 100% success rate, indicating that the system functions as designed. It is expected that this web-based Puskom complaint service system can provide a more structured, efficient, and transparent solution, thereby improving the quality of information technology services at STMIK Widya Cipta Dharma.
Pengembangan Platform LMS Asli Cerdas dalam Upaya Meningkatkan Literasi Digital Anak Usia Sekolah di Kota Samarinda sebagai Mitra Ibu Kota Nusantara (IKN): Innovation of the Asli Cerdas LMS Platform in Enhancing Digital Literacy Among School-Age Children in Samarinda Sa'ad, Muhammad Ibnu; Nursobah; Pajar Pahrudin; Pitrasacha Adytia; Hanifah Ekawati; Salmon
Jurnal Riset Inossa : Media Hasil Riset Pemerintahan, Ekonomi dan Sumber Daya Alam Vol. 7 No. 1 (2025): Juni
Publisher : Badan Perencanaan Pembangunan Daerah, Penelitian dan Pengembangan Kota Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54902/jri.v7i1.171

Abstract

Digital literacy is a fundamental competency in preparing the younger generation to face the challenges of the digital transformation era. Samarinda City, as a strategic partner of the National Capital (IKN), plays an important role in preparing adaptive and tech-literate human resources. This study aims to develop a Learning Management System (LMS) platform called Asli Cerdas to enhance digital literacy among elementary and middle school students. The method used is descriptive-exploratory, involving 675 respondents from 10 districts in Samarinda. Instruments such as digital literacy questionnaires and field observations were used to collect both quantitative and qualitative data. The results showed that students had an average digital literacy index of 3.60, with the lowest score in the aspect of digital safety. Teachers showed better scores but still require technical training to maximize technology use. The Asli Cerdas platform was developed with features including local content, self-assessment tools, and offline access. Pilot testing of the platform showed a 24% improvement in students' understanding of digital literacy. This study recommends the adoption of locally based LMS platforms supported by regional policies to strengthen the digital learning ecosystem in areas that serve as partners to IKN
Comparative Performance Analysis of YOLOv12 and RF-DETR in Face Detection David Hendrawan; Wahyuni; Pitrasacha Adytia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1561

Abstract

Face detection in dense and occluded environments remains a significant challenge in computer vision. This study compares the CNN-based YOLOv12 and the Transformer-based RF-DETR to determine the optimal balance between accuracy and latency for resource-constrained edge computing. Using the WIDER FACE dataset and an NVIDIA T4 GPU, multiple model variants were evaluated. Due to GPU memory constraints during training of the RF-DETR Medium variant, a standardized batch size of 8 was implemented across all models. To ensure methodological rigor, quantitative metrics (precision, recall, F1-score, mAP) were strictly assessed on the validation set. Concurrently, a 100-image subset of the test set was used exclusively for inference efficiency benchmarking, completely separate from detection evaluation. Results indicate YOLOv12X achieved superior overall detection performance (F1-score: 0.764, mAP@50:95: 0.440), significantly outperforming RF-DETR Medium. For real-time applications, YOLOv12M demonstrated the highest efficiency (36.17 FPS vs. 23.32 FPS). Qualitatively, YOLOv12 maintained high sensitivity in crowded scenes, whereas RF-DETR provided stable small-scale face detection despite its lower recall. Overall, under these constrained-hardware conditions, YOLOv12 appears to be a highly viable solution for surveillance systems, while RF-DETR offers a stable alternative for small-object detection when computational overhead and training budgets are less restrictive.
Prediction of the Number of New Students Using the Arima Time Series Model Case Study at Stmik Widya Cipta Dharma Eko Jheremy Oktavianus; Pitrasacha Adytia; Muhammad Ibnu Sa’ad
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2804

Abstract

Planning for new student admissions is an important aspect in university management because it is closely related to strategic decision-making and institutional resource allocation. This study aims to estimate the number of new students in the Informatics and Information Systems Engineering Study Program at STMIK Widya Cipta Dharma using the Autoregressive Integrated Moving Average (ARIMA) method. The data used is secondary data obtained from PDDIKTI with a period of 2015-2025. The analysis process was carried out through several stages, namely stationary testing using the Augmented Dickey-Fuller (ADF) method, model identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), parameter estimation, diagnostic tests, and forecasting processes. The results showed that the best model obtained was ARIMA (0,1,0) after first-order differentiation. The model produces residual that meets the assumption of white noise and is normally distributed. The forecast results show a tendency to decrease the number of new students in the 2026–2028 period. The model evaluation showed a very good level of accuracy in the Informatics Engineering Study Program with a Mean Absolute Percentage Error (MAPE) value of 9.64% and quite good in the Information Systems Study Program of 24.88%. Thus, the ARIMA model (0,1,0) is considered effective in supporting the planning of new student admissions in a more measurable and systematic manner.
RANCANG BANGUN ARTIFICIAL INTELLIGENCE (AI) FINANCIAL AGENT BERBASIS CHATBOT UNTUK MANAJEMEN KEUANGAN PRIBADI MENGGUNAKAN WHATSAPP Dhali Saputra; Pitrasacha Adytia; Muhammad Fahmi
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 9 No. 2 (2026): MISI Juni 2026
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v9i2.2011

Abstract

Rendahnya tingkat literasi keuangan dan ketimpangan akses terhadap layanan keuangan digital masih menjadi permasalahan nyata bagi sebagian besar warga Kota Samarinda. Survei Nasional Literasi dan Inklusi Keuangan (SNLIK) 2024 mencatat indeks literasi keuangan nasional sebesar 65,43 persen, namun adopsi layanan digital belum merata, khususnya di wilayah Kalimantan Timur. Penelitian ini bertujuan merancang, membangun, dan mengevaluasi prototipe agen finansial berbasis kecerdasan buatan yang beroperasi melalui platform WhatsApp sebagai media pengelolaan keuangan pribadi secara mandiri dan berkesinambungan. Metodologi pengembangan memadukan tiga komponen utama: platform otomasi alur kerja n8n sebagai mesin orkestrator, model bahasa besar OpenAI  GPT-5o-mini sebagai prompt engine untuk inferensi dan pemahaman bahasa alami, serta layanan API gateway Fonnte sebagai antarmuka pesan instan WhatsApp. Pendekatan prompt engine dipilih agar sistem mampu mengenali transaksi dalam ragam bahasa percakapan sehari-hari—termasuk singkatan dan istilah lokal—tanpa mengharuskan pengguna mengikuti format perintah yang kaku. Pengujian fungsional dilakukan terhadap dua puluh satu skenario yang mencakup tiga dimensi: pencatatan transaksi harian, sinkronisasi data otomatis ke Google Sheets, dan pemberian rekomendasi keuangan yang dipersonalisasi. Seluruh dua belas skenario berhasil dijalankan dengan tingkat keberhasilan 100 persen. Sistem terbukti mampu mengekstraksi entitas keuangan secara presisi, mempertahankan kesinambungan konteks percakapan antarsesi, dan menyajikan rekomendasi alokasi anggaran otomatis berbasis prinsip 50/20/30. Temuan ini menegaskan bahwa penerapan prompt engine dalam platform pesan instan yang sudah digunakan luas terbukti efektif menekan hambatan adopsi teknologi keuangan digital, sekaligus berpotensi menjadi model solusi inklusi keuangan bagi daerah dengan karakteristik sosiokultural serupa.
Analisis Sentimen Ulasan Pengguna Aplikasi Grab Mobile Menggunakan Metode K-Nearest Neighbor dan Lexicon-Based Maulana Bakti; Pitrasacha Adytia; Bartolomius Harpad
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp402-411

Abstract

Grab is one of the most widely used online transportation and digital service applications in Indonesia. As the number of users grows, reviews provided on the Google Play Store have become an important sourc of information to understand what users think and how satisfied they are. This study aims to study customer feelings about app reviews. Grab uses the K-Nearest Neighbor (KNN) method combined with a Lexicon -Based approach for automatic labeling. The dataset consists of 300 reviews in Indonesian sourced from the Google Play Store. The preprocessing process includes cleaning, case folding, tokenizing, stopword removal, and stemming using the Sastrawi library. Emotion labeling is done automatically using a Lexicon-Based sentiment dictionary. Text attributes are extracted using the TF-IDF (Term Frequency-Inverse Document Frequency) method, then classified using KNN with K = 5 and a training and experimental data sharing ratio of 80: 20. The research findings emphasize that the majority of users (80.67%) give positive reviews to the Grab app. The KNN model achieved 90% accuracy with a precision of 0.92, a recall of 0.96, and an F1-score of 0.94 for the good class. This study demonstrates that the combination of KNN and Lexicon-Based methods can be used effectively in sentiment classification of Indonesian-language reviews.
Application of the Finite State Machine Method in the Desktop-Based “Heroes Of Dawn” RPG Turn-Based Game Muhammad Fachri Sanjaya; Heny Pratiwi; Pitrasacha Adytia
TEPIAN Vol. 2 No. 2 (2021): June 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i2.348

Abstract

FSM (Finite State Machine) is a method of implementing artificial intelligence that is applied to make a decision on NPC (Non Player Character). The application of FSM that is often encountered is to form an NPC with intelligence, so that the NPC can respond to the player's character so that the NPC seems to be able to think. Games have various types (genres) and are increasingly varied in line with the development of hardware and software technology. Writing will focus on games with the Role Playing Game genre or often called RPG. Games in general use Artifical Intelligence in their systems to make the game more interesting to play. Artifical Intelligence is usually applied to NPC (Non Player Character) / Enemy in the game or opponents who must be defeated, one of the applications of Artifical Intelligence in the game to be used in this research is the Finite State Machine (FSM) method.
Development Geographic Information System for Forest Mapping in Kutai Kartanegara Regency Salmon Salmon; Pitrasacha Adytia; Sugih Niansyah; Andriawan; Reza Andrea
TEPIAN Vol. 4 No. 3 (2023): September 2023
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v4i3.2890

Abstract

The agricultural, plantation, and forestry industries that are the main choice of the population to meet household food needs and boost the community's economy are very much in line with the geographical contours of the Regency Kutai Kartanegara. A geographic information system that can provide information on position, location coordinates, forest areas, forest information in Kutai Kartanegara Regency, and paths to find the location of forest areas. A web- based Geographic Information System (GIS) is required to determine the current position and location of the forest. The waterfall method is used to build this GIS framework, which involves stages such as analysis, design, code generation, testing, and maintenance. MySQL is a database management system. PHP, JavaScript, and HTML are used to create programming languages. Bootstrap user interface implementation. Black box testing is used to verify the software. The test results show that the GIS created meets the requirements and can resolve system issues.
Implementasi Arsitektur Keamanan Terintegrasi IDS, WAF Dan FIM Berbasis Wazuh Pada Platform Open Jurnal System Mengacu Pada NIST Cybersecurity Framework William Carey Kornelius; Pitrasacha Adytia; Ahmad Fahrijal Pukeng
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9500

Abstract

The utilization of Open Journal Systems (OJS) as a scientific publishing platform faces significant security threats, including SQL Injection, Cross-Site Scripting (XSS), and webshell injection, which may compromise data integrity and service availability. This study aims to design and evaluate an integrated security architecture based on Wazuh through the implementation of an Intrusion Detection System (IDS), Web Application Firewall (WAF), and File Integrity Monitoring (FIM) using the NIST Cybersecurity Framework approach. The research methodology includes vulnerability identification across 11 journals in 7 universities, the development of a defense-in-depth architecture, and controlled penetration testing based on OWASP Top 10 scenarios. Testing results from 30 attack scenarios demonstrate a 100% detection rate for SQL Injection and webshell injection, and an 80% detection rate for XSS attacks. The system successfully blocks malicious requests with 403 Forbidden responses and generates real-time alerts through centralized log correlation in Wazuh. However, potential false positives were observed in several generic security rules, indicating the need for rule fine-tuning to align with OJS traffic characteristics. Overall, the integrated security approach measurably enhances threat detection and incident response capabilities.
Classification of Diabetes Diseases Based on Medical Features Using Optimized Support Vector Machine Ita Arfyanti; Amelia Yusnita; Pitrasacha Adytia
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8880

Abstract

Diabetes mellitus is a chronic disease caused by impaired glucose metabolism and has become a global health threat with a steadily increasing prevalence each year. According to WHO and IDF, the number of people living with diabetes is projected to reach 783 million by 2045. This condition demands the development of an accurate and efficient early detection system to support medical decision-making. This study aims to develop an optimized Support Vector Machine (SVM)-based classification model to enhance the accuracy and interpretability of diabetes prediction. The dataset used is the Pima Indians Diabetes Dataset, which consists of eight medical features such as glucose level, blood pressure, and body mass index (BMI). The research stages include data preprocessing, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), parameter optimization with GridSearchCV, and interpretability analysis through SHapley Additive exPlanations (SHAP). The results show that the optimized SVM model with the Radial Basis Function (RBF) kernel achieved an accuracy of 82%, with a significant improvement in the diabetes class recall value from 0.564 to 0.83 after optimization. The Area Under Curve (AUC) value of 0.871 indicates the model’s effectiveness in distinguishing between positive and negative classes. The SHAP analysis reveals that Glucose, Age, BMI, and Diabetes Pedigree Function are the most influential features in prediction. These findings emphasize that the combination of normalization, balancing, hyperparameter optimization, and interpretability produces a reliable and transparent SVM model. This model has strong potential for implementation in Clinical Decision Support Systems (CDSS) for accurate and explainable early diabetes detection.
Co-Authors Adha Trisna Lidya Ahmad Fahrijal Pukeng Alvin Hernanda Ekaputra Amelia Yusnita Amelia Yusnita Andriawan Arriyanti, Eka Asmaramany, Dimas Astin, Siti Namira Rizqi Aulia Abdul Rahman Bustomi , Tommy David Hendrawan Dhali Saputra Didi Kuswandi Ekawati Yulsilviana Ekawati, Hanifah Eko Jheremy Oktavianus Fariz Mirza Ahmad Hanifah Ekawati Hanifah Ekawati Harianto, Kusno Harpad, Bartolomius Heny Pratiwi I Nengah Riandika Ika Nurmila Avinda Ita Arfyanti Kasim, Fadly Kelik Sussolaikah Lisda Aulia Rahmi Maulana Bakti Maulana Umar Muhammad Ahsanu Qaulan Muhammad Fachri Sanjaya Muhammad Fahmi Muhammad Firza Fernanda Muhammad Ibnu Saad Saad Muhammad Ibnu Sa’ad Muhammad Ibnu Sa’ad Nurhuda, Asep Nursobah Nursobah, Nursobah Pahrudin, Pajar Pratama, Jerio Putra Rahmat Hidayat Reza Andrea Rizky Zakariyya Rasyad SA'AD, MUHAMMAD IBNU Salmon Salmon Salmon Solly Aryza Sugih Niansyah Supriady Supriady Sussolaikah, Kelik Tommy Bustomi Ujang Sumarwan Wahyuni - Wahyuni wahyuni wahyuni William Carey Kornelius Yudha Satria Yuli Adam Prasetyo Yunita Yunita Yunita Yunita