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J-Intech (Journal of Information and Technology)
Published by Sekolah Tinggi Informatika dan Komputer Indonesia Malang
ISSN : 23031425     EISSN : 2580720X     DOI : https://doi.org/10.32664/j-intech
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science
J-INTECH merupakan jurnal yang diterbitkan oleh Lembaga Penelitian & Pengabdian kepada Masyarakat (LPPM), Sekolah Tinggi Informatika dan Komputer Indonesia Malang. Ruang lingkup jurnal ini pada bidang Teknik Informatika, Sistem Informatika, dan Manajemen Informatika. Tujuannya guna mengakomodasi kebutuhan akan perkembangan Teknologi Informasi. J-Intech terbit setahun dua kali, yaitu Juni dan Desember.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 288 Documents
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Decision Support System for Selecting BPS Central Tapanuli Partners Using the SMART Method Nur, Adzkia; Lubis, Ardilla Syahfitri; Sambora, Dicky; Niska, Debi Yandra
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1857

Abstract

The selection of partners at the Central Statistics Agency (BPS) of Central Tapanuli is a very important process because it determines the quality of supporting staff in census and survey activities. One of the core stages in the selection process is the interview, which functions to directly evaluate the abilities and character of prospective partners. The assessment in the interview covers several main aspects, namely analytical skills, communication, appearance, and politeness. This study aims to design a decision support system based on the SMART (Simple Multi Attribute Rating Technique) method that can help process interview results systematically and objectively. Each criterion in the interview is given a weight based on the level of importance, then the value of each candidate is processed through mathematical calculations that produce a final score. This score is used to determine the candidate's ranking and provide recommendations to the selection committee. The system is developed in the form of a web-based application with a user-friendly interface, and supports data input, value processing, and automatic presentation of results. The implementation results show that the SMART method is able to improve assessment accuracy, reduce subjectivity, and accelerate the decision-making process in partner selection. With this system, the interview process is not only a fairer and more transparent means of assessment, but also supports work efficiency and consistency of selection results in the BPS environment.
Analyzing Students' Interest in Mathematics Through the Implementation of the K-Means Clustering Algorithm Wintana, Dede; Sulaiman, Hamdun; Rohman, Ramdhan Saepul; Gunawan, Gunawan; Ghani, Muhammad Abdul
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1861

Abstract

This Research is motivated by the importance of understanding students' interest in mathematics, especially in State Junior High School 193 East Jakarta, considering that mathematics is often considered a difficult and frightening subject for some students. Learning interest, which is defined as the tendency of students to pay attention with a feeling of pleasure, has a significant influence on the process and results of student learning. This study aims to identify the level of student interest in mathematics using the K-Means algorithm. This method is used to group students into several clusters based on their level of interest. The results showed that students were divided into three clusters, namely the first cluster with very high interest totaling 193 students with an average Final Semester Exam score of 91.920, the second cluster with low interest totaling 18 students with an average score of 52.333, and the third cluster with high interest totaling 66 students with an average score of 87.606.
Application of Naive Bayes Algorithm for Analysis of User Reviews on Mobile Legends Game: Bang Bang Roba, Al-Muchlis Syachrul Ramadani; Lailiyah, Siti; Yusnita, Amelia
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1881

Abstract

Mobile Legends: Bang Bang is a highly popular MOBA game, especially among students, which generates a large volume of user reviews on the Google Play Store. These reviews provide a valuable data source for understanding user sentiment. This study conducts sentiment analysis on user reviews using three variants of the Naïve Bayes algorithm: BernoulliNB, GaussianNB, and MultinomialNB. From an initial 5,000 reviews collected via web scraping using Python, 4,428 reviews were used after neutral reviews were removed to focus solely on positive and negative sentiments.The preprocessing steps included case folding, word normalization, tokenization, stopword removal, and stemming. Sentiment labeling was carried out using a lexicon-based approach, comparing the frequency of positive and negative words in each review. The dataset was split in an 80:20 ratio for training and testing.The results show that MultinomialNB achieved the highest accuracy at 75%, followed by BernoulliNB with 74%, and GaussianNB with 50%. MultinomialNB demonstrated superior performance in detecting positive sentiments, while BernoulliNB offered more balanced results. GaussianNB performed poorly due to its assumption of normally distributed continuous data, which is unsuitable for text classification. This study concludes that Multinomial Naïve Bayes is the most effective model for sentiment analysis of user reviews when working with word frequency-based representations.
Application of K-Nearest Neighbor Algorithm For Sentiment Analysis On Free Fire Online Game Based On Google Play Store Reviews Raya, Dimas; Yusnita, Amelia; Haristyawan, Ivan
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1882

Abstract

The swift expansion of the digital gaming sector, especially online games like Free Fire, has produced extensive user feedback via platforms like the Google Play Store. This research utilizes the K-Nearest Neighbor (KNN) algorithm to conduct sentiment analysis on 5,000 user reviews, with the goal of assessing its classification effectiveness. Following preprocessing (case folding, Text Cleaning, tokenization, stopword Removal, stemming), the data was converted using TF-IDF and balanced through SMOTE. Experimental findings indicate that KNN attained a peak accuracy of merely 36.53% (at k = 14), reflecting weak performance with high-dimensional textual data. In contrast, Logistic Regression attained a notably higher accuracy of 88%, showcasing its dominance for this task. The results offer perspectives for game developers to assess user feelings and emphasize the significance of selecting suitable machine learning models. Future research should investigate advanced classifiers like SVM, Random Forest, or deep learning methods to enhance accuracy.
Development of Geolocation-Based Employee Attendance Application on Android Mobile Kurnia, Nisa; Hananto, April Lia; Tukino, Tukino; Hilabi, Shofa Shofiah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1890

Abstract

The development of mobile-based systems in Indonesia has provided innovative solutions to improve the efficiency of conventional administrative processes, especially in employee attendance. This research aims to develop an Android-based employee attendance application that is integrated with geolocation technology to enable accurate and real-time attendance monitoring. This system is built using the Waterfall method, which includes the stages of needs analysis, system design, implementation using Flutter and Dart programming language, and testing using black box testing techniques. Black-box testing was conducted on six main functions, resulting in a 94% overall success rate. Most functions achieved a 100% pass rate, but two test cases for attendance check in/out failed due to GPS location inaccuracies, highlighting the impact of device and environmental factors. The average response time was 1.28 seconds, and the average GPS delay was 2.1 seconds. The implementation of real-time notifications and admin verification improved transparency and minimized attendance fraud. The results demonstrate that the application provides an effective and efficient solution for employee attendance management. Future work should focus on enhancing location accuracy, conducting non-functional testing, and expanding features to ensure broader adoption and system robustness.
Application Of K-Nearest Neighbor Algoritma for Customer Review Sentiment Analysis at Ngeboel Vapestore Shop Aryanda, Muhammad; Arfyanti, Ita; Yulindawati, Yulindawati
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1893

Abstract

This study applies the K-Nearest Neighbor (K-NN) algorithm to classify customer sentiments from online reviews about Ngeboel Vapestore, a local MSME in the vape industry. A total of 175 reviews from Google Review and Instagram were processed using standard NLP techniques and TF-IDF for feature extraction. The best K-NN model (k=3) achieved 85.4% accuracy. Although Logistic Regression achieved higher accuracy (92.6%), it failed to detect negative sentiment. The findings highlight the potential and limitations of K-NN for sentiment analysis in underexplored MSME contexts like vape retail. The study recommends further model improvements and broader MSME applications.
Development of Academic Community Recommendation System Using Content-Based Filtering at UIN Malang Informatics Engineering Study Program Zuhri, Abdurrozzaaq Ashshiddiqi; Kusumawati, Ririen; Yaqin, Muhammad Ainul; Anwar, Aldian Faizzul; Pahlevi, Achmad Fahreza Alif
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1916

Abstract

The mismatch between the number and quality of Information and Communication Technology (ICT) talents and industry needs in Indonesia creates significant challenges, especially for Informatics Engineering students who often experience difficulties in determining the appropriate professional field. This research aims to develop a content-based filtering-based academic community recommendation system to help students choose communities that are relevant to their interests, skills and experience. The system uses TF-IDF and cosine similarity methods to match student profiles with community descriptions. Data was collected from 48 students and 10 academic communities in the Informatics Engineering Study Program of UIN Malang, and processed through preprocessing stages before modeling. Evaluation results using the System Usability Scale (SUS) resulted in a score of 76, which is categorized in the “good” level, However, users indicated the need for improved guidance in navigating the system. This system is expected to be an innovative solution to increase student participation in appropriate academic communities, as well as support the development of their potential and readiness for the world of work
Development of a Web-Based Decision Support System for Selecting the Optimal Duck Feed Using the Analytic Hierarchy Process (AHP) Nazla, Khaira; Manurung, Enrico Vincentius; Hidayat, M Fauzan; Niska, Debi Yandra
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1918

Abstract

The selection of optimal livestock feed is essential for improving animal health and productivity. This study developed a web-based Decision Support System (DSS) using the Analytic Hierarchy Process (AHP) to help farmers choose the best feed based on nutritional content, price, and availability, including sub-criteria like protein, energy, fat, minerals, and vitamins. The system ranks alternatives using pairwise comparisons and priority weights. Validation against manual calculations showed high accuracy (correlation coefficient: 0.9987; errors <2.5%), with Fermented Feed (A3) as the top choice (score: 0.3611). Both methods produced identical rankings. The system reduces evaluation time by ~85% while maintaining accuracy, proving AHP’s effectiveness in digital livestock feed management tools.
Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks Rahmatmulya, Revaldi; Almais, Agung Teguh Wibowo; Amin Hariyadi, Mokhamad
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1919

Abstract

Natural disasters are events caused by nature such as earthquakes, tornadoes, tsunamis, forest fires, and others. The impacts of natural disasters are significant and varied across various sectors, including the economy, health, and primarily, infrastructure. Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. The data utilized consists of aerial images sourced from xView2: Assess Building Damage, comprising 50 aerial images with 5 classes: no-damage, minor-damage, major-damage, destroyed, and unlabeled. The steps undertaken in this research include data preprocessing using patchify and data augmentation. Subsequently, feature extraction is performed using convolution, followed by the training process using a neural network with the proposed architecture. This study proposes an architecture with 27 hidden layers, with feature extraction utilizing average pooling. The model evaluation process will employ Mean Intersection over Union (MIoU) to assess how closely the segmentation prediction results resemble the original data. The proposed architecture demonstrates the best MIoU result with a value of 0.31 and an accuracy of 0.9577.
Aplikasi Konsultasi Kesehatan Mental dengan Fitur Penjadwalan di Bimbingan Konseling Universitas Teknologi Yogyakarta Heriansaputri, Kevina Maydiva; Romli, Moh Ali
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1922

Abstract

This study developed a mobile-based mental health consultation application at Universitas Teknologi Yogyakarta (UTY) to offer both online and offline counseling services for students. The tool incorporates two primary features online consultations and offline consultation scheduling, designed to improve students' access to mental health support. The system utilizes Flutter for cross-platform mobile application development, Firebase for data management, and Node.js for backend services. The study employs a Research and Development (R&D) methodology encompassing needs analysis, system design, implementation, and testing. The results indicate that the application successfully mitigates obstacles such time limitations, stigma, and restricted accessibility, hence enhancing student involvement with mental health services. The Self-Reporting Questionnaire 20 (SRQ-20) serves as a mental health screening instrument within the application, enabling students to evaluate their mental health status. This program aims to deliver a thorough and accessible solution for mental health counseling at UTY and may serve as a prototype for other universities.

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