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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 487 Documents
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Analisis Komparasi Kinerja Arsitektur MobileNetV3 dan EfficientNet-Lite untuk Klasifikasi Penyakit Padi pada Citra Resolusi Rendah Dading Oktaviadi Resmiranta
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10098

Abstract

National food security relies heavily on rapid and accurate control of rice plant diseases. However, the implementation of automatic detection technology at the farmer level is often hampered by low image quality due to the use of low-spec mobile phone cameras and data compression in areas with poor signal. This study aims to evaluate the performance of two lightweight Deep Learning architectures, MobileNetV3-Small and EfficientNet-Lite, in classifying rice diseases under low-resolution image conditions. The research method applies a simulation of resolution degradation to 128x128 pixels on a dataset consisting of four classes: Blast, Blight, Brown Spot, and Healthy. Empirical test results show that EfficientNet-Lite is significantly superior in diagnostic accuracy with an accuracy of 95.67%, a precision of 95.65%, and a recall of 95.33%. In contrast, MobileNetV3-Small achieved an accuracy of 89.00%, yet offered superior computational efficiency: a model size of only 11.97 MB (73% smaller than EfficientNet-Lite's 45.48 MB) and an inference speed of 4.67 milliseconds per image, equivalent to 214 frames per second (FPS). The study concluded that EfficientNet-Lite is recommended for high-precision diagnostic systems, while MobileNetV3-Small is the most adaptive solution for real-time applications on storage-constrained mobile devices.
Identifikasi Faktor Determinan Keterlibatan Pemain Game Online Berbasis Perilaku Menggunakan Machine Learning Raid Alvaro Fathin Lie; Sindhu Rakasiwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10186

Abstract

Along with the rapid growth of the online gaming industry and the increasing complexity of player behavioral patterns, player engagement analysis has become a critical component in designing data-driven retention strategies. This study aims to identify the determinant factors influencing player engagement levels in online games and to develop an accurate classification prediction model. The study is motivated by the limitations of conventional engagement analysis approaches that rely solely on total playtime, which tend to be biased and do not fully represent player loyalty. A quantitative research approach was employed using a behavioral dataset consisting of 40,034 players, with a comparative evaluation of Naïve Bayes, Logistic Regression, and Random Forest algorithms. The data preprocessing stage included variable encoding, feature scaling, and data partitioning using a stratified train–test split to preserve class distribution. Hyperparameter optimization for the Random Forest model was performed using Grid Search with 5-fold cross-validation to objectively determine the optimal parameter combination. Experimental results demonstrate that the Random Forest algorithm achieved the best performance with a test accuracy of 91.30%, outperforming Naïve Bayes (83.61%) and Logistic Regression (82.64%). Feature importance analysis revealed that demographic factors and total playtime contributed relatively little to player engagement prediction, whereas weekly gaming session frequency and average session duration were identified as the most influential determinants. The findings suggest that player retention strategies are more effective when shifting from a duration-based approach to a frequency-based approach, encouraging consistent player interaction and fostering long-term engagement.
Prediksi Harga Saham Bank BRI Menggunakan Metode ANFIS dengan Penentuan Input Berbasis ACF dan PACF Adelia Rova Chumairo; Aliya Octavia Ramadhani; Rachma Raudhatul Jannah; Dian Candra Rini Novitasari
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10142

Abstract

Peramalan harga saham merupakan aspek penting dalam mendukung pengambilan keputusan investasi di pasar modal mengingat karakteristik data harga saham yang bersifat dinamis dan berfluktuasi. Penelitian ini bertujuan untuk membangun model prediksi harga saham PT Bank Rakyat Indonesia (Persero) Tbk. menggunakan metode Adaptive Neuro-Fuzzy Inference System (ANFIS) dengan penentuan variabel masukan berdasarkan analisis Autocorrelation Function (ACF) dan Partial Autocorrelation Function (PACF). Data yang digunakan berupa data harga saham harian periode 3 Januari 2022 hingga 27 Oktober 2025. Sebelum proses pemodelan, data dinormalisasi untuk menyamakan skala nilai dan meningkatkan kestabilan proses pembelajaran. Selanjutnya, lag signifikan hasil analisis ACF dan PACF digunakan sebagai input dalam pembentukan dataset supervised learning pada ANFIS. Evaluasi kinerja model dilakukan menggunakan Mean Absolute Percentage Error (MAPE) dengan beberapa variasi learning rate. Hasil penelitian menunjukkan bahwa konfigurasi terbaik diperoleh pada learning rate sebesar 0,01 dengan nilai MAPE pengujian sebesar 1,295085% yang termasuk dalam kategori sangat baik, serta hasil prediksi mampu mengikuti pola data aktual, sehingga metode ANFIS yang dikombinasikan dengan seleksi input berbasis ACF dan PACF dinilai efektif dalam merepresentasikan dinamika pergerakan harga saham.
Implementasi Metode FIFO Pada Sistem Pengelolaan Pengaduan Berbasis Web Kampus UPI Purwakarta Anisa Nursaidah; Hafiyyan Putra Pratama; Ahmad Fauzi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.9242

Abstract

 The implementation of integrity zone initiatives at UPI Purwakarta requires a transparent and accountable complaint management system. However, complaint handling is still conducted through Google Forms and manual processes, which may lead to limited service transparency, delays in handling, and unclear report status for complainants. This condition indicates the need for a system capable of managing complaint processing in a systematic and structured manner. This study aims to design and implement a web-based complaint management system by applying the First In First Out (FIFO) method to ensure that reports are received and processed fairly based on their arrival time. The system was developed using the ADDIE model, consisting of Analyze, Design, Development, Implementation, and Evaluation stages. System evaluation was carried out using black-box testing and the System Usability Scale (SUS). The black-box testing results showed a 100% functional success rate, while SUS testing involving 30 respondents obtained a score of 77.75, which falls into the good and acceptable category. These findings indicate that the implementation of the FIFO method improves the systematic handling of complaints and supports service transparency. This study contributes to the application of time-based prioritization mechanisms to enhance fairness and transparency in complaint management at UPI Purwakarta.
Optimalisasi Desain Antarmuka Aplikasi melalui Penerapan Metode Double Diamond dan Analisis Ulasan Berbasis Web Scraping Rudy Sofian; Iklima Nur Mufida; Vito Hafizh Cahaya Putra; Bakti Bestin; Bella Syifa Anandita Suherlan
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10180

Abstract

The system development process requires attention from various aspects, one of which is the design of the User Interface (UI) and User Experience (UX). UI/UX design plays an important role in determining user comfort and engagement. One application that has experienced problems in terms of UI/UX is iPusnas, a digital library application owned by the National Library of Indonesia, which has received low reviews on the Playstore and Appstore platforms. This study aims to redesign the iPusnas application using the Double Diamond (DD) method and analyze user reviews through Web Scraping and Natural Language Processing (NLP). A total of 16,725 reviews from Playstore were analyzed to identify the main usability issues. The results of the analysis formed the basis for the application of the four stages of the DD method, namely discover, define, develop, and deliver, to produce a new interface design that meets user needs. System Usability Scale (SUS) testing on 200 respondents resulted in an average score of 81, categorized as acceptable (B), with a satisfaction level ranging from good to excellent. These results indicate that the application of NLP-based Web Scraping and the DD method proved effective in supporting the application redesign process, improving usability quality, and enhancing the user experience on the iPusnas application
Sistem Pakar Diagnosa Penyakit Mulut dan Gigi Menggunakan Convolutional Neural Network dan Metode Forward Chaining Muzakkir Pangri; Muhammad Yusuf; Julius Janhandry Kwasua; Aziz Gusti Pratama; Trinuzuliati Langguhe
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10231

Abstract

Kesehatan gigi dan mulut memiliki peran penting dalam menjaga kualitas hidup serta kesehatan tubuh secara keseluruhan, namun keterlambatan diagnosis penyakit gigi dan mulut masih sering terjadi akibat keterbatasan akses terhadap tenaga medis serta rendahnya kesadaran masyarakat terhadap pentingnya pemeriksaan dini. Penelitian ini bertujuan mengembangkan sistem pakar diagnosis penyakit gigi dan mulut berbasis Android dengan mengintegrasikan metode Convolutional Neural Network berarsitektur VGG16 sebagai pengklasifikasi citra klinis dan metode Forward Chaining sebagai mesin inferensi untuk memberikan rekomendasi penanganan awal. Dataset yang digunakan berjumlah 1750 citra klinis gigi dan mulut yang terbagi ke dalam lima kelas penyakit, yaitu gingivitis, dental calculus, caries, ulcer, dan hypodontia, dengan masing-masing kelas terdiri dari 350 citra. Dataset dibagi menjadi 70 % data pelatihan, 15 % data validasi, dan 15 % data pengujian guna memastikan proses pembelajaran dan evaluasi model berjalan optimal. Hasil pelatihan menunjukkan bahwa model mampu mencapai akurasi klasifikasi sebesar 98 %. Evaluasi kinerja model yang dilakukan berdasarkan classification report mendapatkan, nilai precision pada kisaran 95%–100%, recall pada kisaran 92%–100%, dan f1-score pada kisaran 95%–100%. Secara keseluruhan, model mencapai akurasi 98%, yang menunjukkan performa klasifikasi yang baik dan konsisten. Integrasi hasil klasifikasi Convolutional Neural Network dengan metode Forward Chaining memungkinkan sistem memberikan diagnosis awal serta rekomendasi penanganan relevan berdasarkan aturan medis. Dengan demikian, sistem ini efektif sebagai alat bantu deteksi dini penyakit gigi dan mulut berbasis citra melalui perangkat seluler bagi masyarakat wilayah dengan keterbatasan layanan kesehatan.
Penerapan Aspect Based Sentiment Analysis pada Produk GPU Joshua Aprivaldis Toelle; Imanuel Jeremiah Garis Ramba; Fajar Akhbarudin Rosnah Wangge; Sebastianus Adi Santoso Mola
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.9039

Abstract

Meningkatnya permintaan Graphics Processing Unit (GPU) yang didorong sektor gaming, komputasi awan, dan artificial intelligence menciptakan tantangan bagi konsumen dalam memilih produk yang sesuai kebutuhan, di mana ulasan produk konvensional cenderung terbatas pada aspek performa dan kurang memberikan analisis komprehensif terhadap berbagai dimensi produk. Penelitian ini bertujuan menganalisis sentimen pengguna terhadap aspek-aspek spesifik produk GPU menggunakan pendekatan Aspect Based Sentiment Analysis (ABSA) untuk memberikan wawasan yang lebih mendalam kepada calon pembeli. Metodologi penelitian menggunakan pendekatan kuantitatif berbasis Natural Language Processing dengan implementasi model RoBERTa pre-trained untuk menganalisis data dari platform Reddit, khususnya subreddit r/GPU, r/buildapc, dan r/hardware, dengan fokus perbandingan Nvidia GeForce RTX 5070 Ti dan AMD Radeon RX 9070 XT pada lima kategori aspek: performance, power consumption, compatibility, price, dan future-proof. Hasil penelitian menunjukkan bahwa model RoBERTa mampu mengklasifikasikan sentimen dengan akurasi tinggi pada setiap aspek yang dianalisis, dengan RTX 5070 Ti menunjukkan sentimen positif dominan pada aspek performance dan future-proof, sedangkan RX 9070 XT unggul pada aspek price dan power consumption. Penelitian ini membuktikan efektivitas ABSA dalam memberikan analisis sentimen yang lebih granular dan dapat membantu konsumen membuat keputusan pembelian yang lebih informatif.

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