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Infotech: Journal of Technology Information
Published by STMIK Widuri
ISSN : 26205181     EISSN : 24602108     DOI : https://doi.org/10.37365/it
Core Subject : Science,
Computer Science & IT
Jurnal Infotech adalah jurnal ilmiah yang berisi hasil penelitian yang ditulis oleh dosen, peneliti dan praktisi. Jurnal ini diharapkan untuk mengembangkan penelitian dan memberikan kontribusi yang berarti untuk meningkatkan sumber daya penelitian di bidang Teknologi Informasi dan Ilmu Komputer. Infotech diterbitkan oleh Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Widuri dengan akses terbuka. Setiap artikel yang diterbitkan memiliki pengidentifikasi objek digital (DOI). ISSN 2620-5181 (Online) ISSN 2460-2108 (Print)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 228 Documents
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ANALISIS SENTIMEN DAN STRUKTUR SOSIAL DALAM PERDEBATAN DARING MENGENAI KEBIJAKAN MAKAN BERGIZI GRATIS (MBG) DI INDONESIA Jusia Amanda Ginting; I Gusti Ngurah Suryantara; Agustinus Fritz Wijaya; Teady Matius Surya Mulyana; Raphael Benedict Manuel
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.596

Abstract

Public policy discussions increasingly take place on social media, where public opinion is shaped not only by message content but also by patterns of user interaction. This study analyzes online conversations related to Indonesia’s Free Nutritious Meal (MBG) policy on platform X by integrating Social Network Analysis (SNA) and sentiment analysis. The dataset consists of 3,459 tweets collected between January 8 and February 11, 2025. Communication networks were constructed based on reply and mention relationships to identify interaction patterns and influential accounts. Sentiment analysis was conducted using a Natural Language Processing approach, with initial labeling based on BERT and further classification using a Support Vector Machine (SVM). Model performance was evaluated using accuracy, achieving a score of 91.78%. The findings reveal that MBG discussions form a relatively sparse yet highly centralized network dominated by a small number of accounts. Most tweets express neutral sentiment, while temporal analysis indicates a significant spike in activity on February 10, 2025. This study demonstrates that integrating network and sentiment analysis provides a more comprehensive understanding of how public opinion evolves in digital environments.
PENGARUH GAMIFIKASI TERHADAP PENINGKATAN KESADARAN KEAMANAN SIBER DAN KEBIASAAN DIGITAL DI UNIVERSITAS XYZ Raphael Benedict Manuel; Jusia Amanda Ginting
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.578

Abstract

The increasing number of cybercrime cases indicates a low level of digital security awareness among the public. Therefore, an interactive and engaging educational approach is needed to better understand the importance of safe behavior in cyberspace. This study aims to develop and test the effectiveness of educational media-based learning media, namely gamification, in increasing cybersecurity awareness. The gamification is designed as an exploration game, watching educational videos inside, and ending with an evaluative quiz. The research method used is an experiment with a pretest and posttest design, where respondents were given a pretest before the intervention with the learning media and a posttest after the intervention. Analysis of the results showed a significant increase in posttest scores compared to the pretest, indicating that gamification is effective in increasing user knowledge and awareness of cyber threats. These findings indicate that implementing game elements in learning media can be an effective and engaging strategy in building a digital security culture.
SISTEM INFORMASI MONITORING GLUKOSA DAN LAYANAN KESEHATAN TERINTEGRASI Era Sari Munthe; Purwo Agus Sucipto
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.605

Abstract

The advancement of digital technology in the healthcare sector requires systems capable of supporting real-time and integrated patient monitoring, particularly in the management of chronic diseases such as diabetes mellitus. This study aims to develop and evaluate an integrated glucose monitoring information system to enhance monitoring effectiveness and improve clinical decision-making quality. The research employs a mixed methods approach with a sequential explanatory design, combining quantitative analysis through system performance measurement and qualitative analysis through exploration of user experiences. The results indicate that the developed system demonstrates high usability, consistent data accuracy, and responsive access speed, thereby improving patient adherence to monitoring and increasing the efficiency of healthcare professionals in service delivery. Qualitative findings also reveal that the system facilitates real-time access to health information and strengthens communication between patients and healthcare providers. This study contributes to the development of an integrated and user-adaptive health information system model and offers practical implications for supporting digital transformation in technology-based healthcare services.
DAMPAK ADOPSI KECERDASAN BUATAN TERHADAP KINERJA USAHA MIKRO, KECIL, DAN MENENGAH (UMKM) Sucipto Basuki; Riyanto Riyanto; I Ketut Sudaryana; Jan Everhard Riwurohi
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.593

Abstract

The advancement of Artificial Intelligence (AI) has significantly accelerated digital transformation across various sectors, particularly Micro, Small, and Medium Enterprises (MSMEs). This Research aims to investigate the effects of AI integration on the operational efficiency of MSMEs in the Cibitung District of Bekasi Regency. Empirical data were gathered through a survey of MSME stakeholders, using a meticulously structured questionnaire, and subsequently analyzed using data-driven methodologies within the Orange Data Mining application. The analytical process encompassed data preprocessing and correlation analysis. The results reveal a positive correlation between AI integration and MSME operational performance. A correlation coefficient of 0.726 indicates a robust positive association between AI adoption and MSME sales performance, whereas an R² of 52.7% indicates that the model exhibits moderate to good predictive capability in explaining variations in MSME performance. These findings suggest that adopting artificial intelligence can enhance operational efficiency, boost business productivity, and expand MSMEs’ market reach. This study enriches the existing literature by proposing an analytical framework grounded in Orange Data Mining as a viable alternative to conventional analytical methodologies in MSME Research, while simultaneously underscoring the practical implications for digital transformation strategies and policy formulation aimed at facilitating AI adoption within the MSME sector.
PERANCANGAN UI/UX APLIKASI SELF-REFLECTION BERBASIS MOBILE UNTUK MENINGKATKAN KESADARAN EMOSI PADA MAHASISWA Safina Nur Rahmah
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.621

Abstract

Mental health has become an issue of increasing concern on social media, particularly among young people. According to the Global Student Survey 2025 conducted by Chegg, 56% of university students in Indonesia experience academic burnout, while the government estimates that approximately 30% of Indonesia's 280 million population suffer from mental health disorders. These conditions underscore the importance of preventive efforts that can help individuals recognize their emotional states at an early stage. Self-reflection practices offer a constructive approach, allowing individuals to evaluate their experiences, thoughts, and feelings. Advances in digital technology present opportunities to provide media that supports the self-reflection process more practically and accessibly. This study aims to design the UI/UX of a mobile-based self-reflection application to help enhance emotional awareness among university students. Design thinking was employed as the methodology in this study to produce a UI/UX design that aligns with user needs and preferences. Testing was conducted using the System Usability Scale method to evaluate the usability and user satisfaction of the designed prototype. The test results yielded an average SUS score of 97.5, which falls under the excellent category, indicating that the developed application design successfully meets user needs and preferences.
ANALISA DAN PERANCANGAN APLIKASI PENYETORAN SAMPAH MENJADI LISTRIK : SEEKLUS Jessicania Windari; Rhisa Adika Putri
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.599

Abstract

The waste problem in Indonesia has reached an emergency condition with an estimated generation of 69.7 million tons by 2025. Although digital solutions are starting to emerge, logistics efficiency in conventional waste collection systems is still a major obstacle. The research aims to design "Seeklus", an innovative self-waste management system that integrates a mobile application with a Trash Vending Machine based on electricity voucher incentives. The method used is Design Science Research (DSR) with the SDLC Prototyping stage, which is supported by SWOT strategy analysis based on data from 150 questionnaire respondents. The results of the study show that the main strategy is focused on utilizing the novelty of the digital incentive system to increase the participation of urban communities. User experience evaluation using the User Experience Questionnaire (UEQ) resulted in very positive scores on the Attractiveness (4.45) and Novelty (4.40) dimensions. These findings prove that Seeklus has successfully offered innovative, efficient, and high user acceptance solutions as part of the smart city ecosystem and circular economy.
PERANCANGAN APLIKASI SOFTWARE DEFECT DETECTION DENGAN ALGORITMA BACKPROPAGATION, PCA DAN SVM Mangapul Siahaan; Rubin Rubin; Syaeful Anas Aklani
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.579

Abstract

Software defects are a major issue in software development because they can affect the quality, reliability, and performance of a system. As digital technology advances at an increasingly rapid pace, software complexity is also rising, thereby increasing the likelihood of software defects. This study aims to apply and compare several machine learning and artificial intelligence methods for detecting software defects. The methods used in this study include Support Vector Machine (SVM), Principal Component Analysis (PCA) as a dimension reduction technique, and Backpropagation as a neural network-based method. The research process was conducted through a series of experiments to evaluate and compare the performance of each method based on the accuracy values obtained. The results show that the combination of SVM and PCA provides the best performance in detecting software defects compared to other methods. The highest accuracy obtained was 85.78% when using 13, 15, and 16 PCA components. Meanwhile, SVM without PCA achieved an accuracy of 85.47%, and Backpropagation achieved an accuracy of 84.83%. These results indicate that the application of PCA is capable of improving SVM classification performance through a dimension reduction process that preserves important features in the dataset. However, the performance achieved is still influenced by the characteristics of the dataset, the data distribution, and the model configuration used.
APLIKASI KLASIFIKASI PENYAKIT RETINA BERBASIS DESKTOP MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) Baharudin Yusup Habibi; Karno Diantoro; Samroh Samroh; Ilham Aditya Chandra; Ahmad Soderi
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.603

Abstract

Retinal diseases such as choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen are the leading causes of vision impairment and require early detection through optical coherence tomography (OCT) imaging. The diagnostic process, which is performed manually by ophthalmologists, is relatively time-consuming and may lead to delays in treatment. This study aims to develop a Convolutional Neural Network (CNN)-based retinal condition detection application integrated with a desktop application to assist in the automatic analysis of OCT images. The data used comes from the Kermany OCT Dataset, which consists of 30,000 retinal images divided into four categories: CNV, DME, drusen, and normal. The research stages include image preprocessing, such as resizing to 224×224 pixels, normalization, contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), and data augmentation. The CNN model was developed using Python with the TensorFlow and Keras libraries to extract image features and classify retinal conditions. Test results show that the CNN model achieved an accuracy rate of 99.73% in classifying retinal OCT images. The trained model was then integrated into a Java-based desktop application so it can be used as a diagnostic support system to facilitate faster and more consistent retinal image analysis. The results of the study indicate that the CNN method is effective for classifying retinal diseases and has the potential to support the early detection of retinal diseases based on OCT images.

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