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Agus Junaidi
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INDONESIA
Jurnal Informatika
Published by Universitas Bina Sarana Informatika
ISSN : 23556579     EISSN : 25282247     DOI : https://doi.org/10.31294/informatika
Core Subject : Science,
Computer Science & IT
Jurnal Informatika first publication in 2014 (ISSN: e. 2528-2247 p. 2355-6579) is scientific journal research in Informatics Engineering, Informatics Management, and Information Systems, published by Universitas Bina Sarana Informatika which the articles were never published online or in print. The publication is scheduled twice a year (April and October). The Editor welcomes submissions of manuscripts that relate to the field. Jurnal Informatika respects all researchers Technology and Information field as a part spirit of disseminating science resulting and community service that provides download journal articles for free, both nationally and internationally. The editorial welcomes innovative manuscripts from Technology and Information field. The scopes of this journal are: Expert System, Decision Support System, Data Mining, Artificial Intelligence System, Machine Learning, Genetic Algorithms, Business Intelligence and Knowledge Management, and Big Data.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 18 Documents
 1   2 
Implementation of DSS for the Selection of MSME Financing Based on Financial Performance and Growth Potential Willy Eka Septian; Maya Permata Sari; Rinaldi Adam; Muhammad Ibrahim; Silvia Maharani; Lena Magdalena
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ji.v13i1.11151

Abstract

Many businesses have difficulty choosing a funding scheme that suits their financial condition and their company's growth potential. The Analytical Hierarchy Process (AHP) method and the Order of Preference by Similarity to Ideal Solution (TOPSIS) technique are combined to create the right Decision Support System (DSS). This research collected data through surveys and interviews with experts in 30 relevant agency-assisted MSMEs, but the data that will be displayed in this journal are as many as 5 MSME data that we have selected. Then, using AHP to determine how important financial criteria and business prospects are, and using the TOPSIS method to compare alternative financing such as people's business loans, fintech loans, and venture capital. Research outputs include a prototype of a system that can generate scientific publications, policy inputs, and objective financing advice for financial institutions. It is hoped that with this DSS, the financing selection process will be more measurable and accurate, and will support the sustainable growth of MSMEs. This resualt shows that  the Analytical Hierarchy Process (AHP) method  is able to produce a consistent criterion weight with  a Consistency Ratio (CR) value of < 0.1, where business duration (0.22) and income (0.17) are the most dominant factors in assessing the feasibility of MSME financing. Furthermore, the TOPSIS method  was used to produce an objective MSME ranking, with Krh Kreasikus Production obtaining the highest preference value (Ci = 0.6911) and Nainay Salted Egg Producer obtaining the lowest value (Ci = 0.2259), demonstrating the ability of this method in distinguishing at-risk MSMEs and MSMEs with better growth potential. Overall,  the AHP-TOPSIS-based Decision Support System model has proven to be effective in helping financial institutions evaluate MSMEs in a more systematic, transparent, and data-based manner  compared to conventional methods.
Implementation of IndoBERT Model in Predicting Anxiety Disorders from Comments on Social Media Rekha Inaya Putri; Lathifah Alfat
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ji.v13i1.11213

Abstract

The development of social media as a means of emotional expression has opened up new opportunities in the early detection of mental health disorders, particularly anxiety disorders, which are still rarely analyzed using Indonesian-language computational approaches. To implement and evaluate IndoBERT model in detecting indications of anxiety disorders based on Indonesian-language social media comments. The method of study used an experimental quantitative approach with a total of 6,075 comments collected from Twitter, Instagram, and TikTok, which were classified into two categories: anxiety and normal. Pre-processing processes were carried out through text cleaning, slang normalization, and stopword removal before the IndoBERT model was trained using fine-tuning techniques for three epochs. Model performance was tested using accuracy, precision, recall, and F1- score metrics, and evaluated through confusion matrix analysis and k-fold cross-validation to ensure consistency of results. The results show that IndoBERT achieved 99.67% accuracy, 0.98 precision, 0.96 recall, and 0.97 F1-score in the anxiety class, with very low classification errors. This performance demonstrates that the model is able to effectively recognize linguistic patterns of anxiety despite data imbalance between classes. These findings confirm IndoBERT’s potential as a basis for developing a text-based early detection system for anxiety disorders in Indonesia. It is recommended for future studies to expand the data sources, add other psychological disorder categories, and compare performance with other algorithms to improve the model’s reliability.    
Mapping Building Outdoor to Analysis Noise Leq with Matlab and 3DField Risky Via Feriyanti
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ji.v13i1.11251

Abstract

Noise Level Standards are the maximum limit of noise levels that are permitted to be released into the environment from businesses or activities so that they do not cause harm to human health and environmental comfort. Sound Level Meter is a tool used to measure noise, unwanted sounds, or those that can cause pain in the ears. Sound Level Meters are usually used in work environments such as the aviation industry, malls, campuses and so on. This time the Sound Level Meter was used to measure noise in the Animal Health Building environment at Gadjah Mada University which was carried out on Friday 17 February 2017 from 13.00 WIB to 17.00 WIB. Sound Level Meter is used to measure noise between 30-130 dB in dB units from frequencies between 20-20,000Hz. Research was also carried out during the Practicum Class for the Analysis and Instrumentation Course. The research is carried out by holding the Sound Level Meter parallel to the practitioner's head and the results will be obtained after the Sound Level Meter sounds by waiting for an interval of 5 seconds when the dB value in the Sound Level Meter is stable, then the resulting dB value is written on a sheet of paper that has been made in a table. The aim of the practicum is so that students can understand the sound phenomena that occur in our environment, understand the level of noise that occurs in our environment and understand how the Sound Level Meter works clearly.
Accuracy Comparison of Support Vector Machine and K-Nearest Neighbors in Face Recognition for Library User Identification Ellyza Hardianty; Mohammad Nasucha
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ji.v13i1.11424

Abstract

Traditional library book lending systems that rely on membership cards or personal IDs are prone to misuse due to human error. To address this, this study developed a web-based book lending application using face recognition enabling automatic user verification without physical cards, improving security, and reducing human errors. In this research 10 university students took roles as the application’s users. The goal is that the application is able to identify every library user who is going to borrow or return books based on their real time face image. The face recognition itself has been developed using dlib’s face detection, cropping, and feature extraction functions and Support Vector Machine (SVM) classification model. The K-Nearest Neighbors (KNN) model was also tested to for classification accuracy comparison. Model validation tests show that the dlib works well in detecting face location within an image, cropping the face area, and extracting face features while the two classification models are able to well classify student IDs too. The SVM model results in 91% accuracy, 90% precision, 91% recall, and 91% F1-score, which is however slightly better than KNN’s 89% accuracy, 89% precision, 88% recall and 88% F1-score. The SVM has been then chosen for the application. Following the completion of application development, a system test has been conducted with black box method and returns with system accuracy of 90%. This finding confirms that implementing dlib and an SVM model for user identification for an application can be a promising method. 
Semi-Supervised Bullying Detection in Narrative Student Counselling Reports Using a Hybrid CNN-LSTM with Pseudo-Labelling Suwarno Suwarno; Muthia Andini; Mangapul Siahaan
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ji.v13i1.11512

Abstract

Bullying incidents in schools are often documented in narrative student counselling reports containing informal language, emotional expressions, and contextual dependencies, which pose challenges for automated text classification, particularly under limited labeled data conditions. This study aims to develop a bullying detection model for narrative student counselling reports using a Hybrid CNN-LSTM architecture combined with a pseudo-labelling-based semi-supervised learning approach. The proposed model is trained through a two-stage process, consisting of pre-training on approximately 70,000 publicly available abusive-language texts and fine-tuning using 1,000 anonymized student counselling reports validated by guidance counsellors. Pseudo-labelling is employed to expand the training data while preserving domain relevance and adhering to ethical considerations. Experimental results show that the proposed model achieves an accuracy of 0.8698, a recall of 0.8570, and an F1-score of 0.7951. Although the precision value (0.7415) is relatively lower, higher recall is prioritized to reduce the risk of overlooking potential bullying cases in the school counselling context. Comparative analysis with Logistic Regression and Linear SVM indicates that the Hybrid CNN-LSTM model demonstrates more stable performance when processing longer narrative inputs that require contextual interpretation. This study contributes empirical evidence on the effectiveness of semi-supervised deep learning for bullying detection in low-resource, narrative student counselling data, a setting that remains underexplored in prior work.
Opinion Mining on Spotify Music App Reviews Using Bidirectional LSTM and BERT Primandani Arsi; Reza Arief Firmanda; Iphang Prayoga; Pungkas Subarkah
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

The increasing number of user reviews on digital music platforms such as Spotify highlights the importance of sentiment analysis to better understand user perceptions. This study aims to develop a sentiment classification model for Spotify user reviews using a Bidirectional Long Short-Term Memory (BiLSTM) approach combined with BERT embeddings. The dataset consists of multilingual user reviews collected from the Google Play Store. Preprocessing steps include text cleaning, tokenization, and padding. BERT is utilized to generate contextual word embeddings, which are then processed by the BiLSTM model to classify sentiments as either positive or negative. The model’s performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the BiLSTM-BERT model achieves an F1-score of 0.8852, a recall of 0.9396, a precision of 0.8375, and an accuracy of 0.8374. These findings demonstrate the model’s effectiveness in handling multilingual sentiment analysis tasks, offering valuable insights for developers in enhancing user experience through data-driven decision-making.
SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis Bahtiar Imran; Selamet Riadi; Emi Suryadi; M. Zulpahmi; Zaeniah Zaeniah; Erfan Wahyudi
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Bug detection in Python programming is a crucial aspect of software development. This study develops an automated bug detection system using feature extraction based on Abstract Syntax Tree (AST) and a Random Forest Classifier model. The dataset consists of 100 manually classified bugged files and 100 non-bugged files. The model is trained using structural code features such as the number of functions, classes, variables, conditions, and exception handling. Evaluation results indicate an accuracy of 86.67%, with balanced precision and recall across both classes. Confusion matrix analysis identifies the presence of false positives and false negatives, albeit in relatively low numbers. The accuracy curve suggests a potential overfitting issue, as training accuracy is higher than testing accuracy. This study demonstrates that the combination of AST-based feature extraction and Random Forest can be an effective approach for automated bug detection, with potential improvements through model optimization and a larger dataset.
Performance Evaluation of LSTM and GRU Models for Movie Genre Classification Based on Subtitle Dialogs Using Augmented Data and Cross-Validation Ni Luh Putu Yonita Putri Utami; Desy Purnami Singgih Putri; Ni Kadek Dwi Rusjayanthi
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

This study aims to evaluate and compare the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in classifying movie genres based on subtitle dialogs. To address data imbalance across genres, data augmentation was applied to create balanced datasets with 500 and 700 samples per genre, in addition to the original dataset. The classification models were built using Word2Vec for word embedding, followed by LSTM and GRU architectures with a single hidden layer and dropout regularization. Model performance was assessed using accuracy and further validated through 5-fold cross-validation. The best test accuracy was achieved with the dataset containing 700 samples per genre, reaching 91% for LSTM and 92% for GRU. Cross-validation showed stable performance with average accuracies of 0.68 for LSTM and 0.67 for GRU. A paired t-test analysis yielded a p-value of 0.341, indicating no statistically significant difference between the two models. These findings suggest that both LSTM and GRU are effective for genre classification based on subtitle dialogs. The use of data augmentation is a key contribution of this study, enabling improved model performance on underrepresented genres. This research supports the development of automated movie recommendation systems that utilize subtitle-based genre prediction.
Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application Eka Rahmawati; Adi Wibowo; Budi Warsito
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Supporting children’s early development requires consistent attention, ensuring their growth aligns with health standards. PrimaKu is one of the mobile applications developed by the Indonesian Pediatric Society. That application was created to assist parents in recording developmental milestones, monitoring immunization schedules, and accessing practical health information. This study investigates user perceptions of the application by analyzing publicly available reviews and ratings from the Google Play Store. Four supervised machine learning algorithms were applied to classify the sentiment expressed in the reviews: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. Among the models tested, SVM achieved the highest classification accuracy (81%), followed by Random Forest (77%), Decision Tree (74%), and Naive Bayes (73%). Precision, recall, and F1-score were also used to evaluate the performance of each model. The results highlight the relevance of machine learning in capturing and interpreting user sentiment toward digital health tools. Further exploration of deep learning architectures is encouraged to enhance classification accuracy and understanding of features.
Batik Pattern Classification Using Logistic Regression, SVM, and Deep Learning Features Ratih Addina Hapsari; Imam Yuadi
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

This study presents the integration of deep learning-based feature extraction with conventional machine learning classifiers for automatically categorizing Indonesian batik patterns. The research utilizes five traditional motifs: Alas Alasan, Kokrosono, Semen Sawat Gurdha, Sido Asih, and Sido Mulyo. Feature extraction was conducted using three deep learning models: Inception V3, VGG16, and VGG19, followed by classification through Logistic Regression and Support Vector Machines (SVM), with data processing performed in Orange. Experimental results show that Inception V3 combined with Logistic Regression achieved the highest classification performance, reaching 99.2% classification accuracy and an F1-score of 0.992. These results confirm the effectiveness of deep feature embeddings in improving the automatic classification of batik motifs. The study contributes to developing intelligent classification frameworks, offering a scalable approach to cultural heritage preservation through technology. Future work will focus on enhancing feature extraction methods and expanding the dataset to address motif overlap challenges.

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