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Journal Software, Hardware and Information Technology
Published by Universitas Islam Negeri Alauddin Makassar
ISSN : 28083385     EISSN : 27768961     DOI : -
Core Subject : Science,
Computer Science & IT
Journal Software, Hardware, and Information Technology (SHIFT) is an academic journal published in January and June by State Islamic University Alauddin of Makassar and managed by Department of Information System, Faculty of Science and Technology. The journal publish scholarly articles on the scope of Computer Architecture; Virtual/Augmented Reality; Computer Security, Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods); etc. Every manuscript submitted to Journal SHIFT is independently reviewed by at least two reviewers in the form of "peer-review". It is due to increase the quality of articles. Decision for publication, amendment, or rejection is mostly based upon their reports/recommendation. In certain cases, the editor may submit an article for review to another, third reviewer before making a decision, if necessary. The Editorial Team, however, reserves the right to make the final decision on the status of the manuscript with regard to publication.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 60 Documents
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Metode Algoritma Logistic Regression dalam Klasifikasi Email Spam Purnama, Asep; Hamidin, Dini
Journal Software, Hardware and Information Technology Vol 5 No 1 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i1.159

Abstract

This study aims to implement the Logistic Regression algorithm in spam email classification using an experimental method. Using a dataset of 4,073 emails categorized as spam and non-spam, the research involves several stages, including data preprocessing, feature extraction using the TF-IDF method, and the application of Logistic Regression for classification. The experimental evaluation of the model shows excellent performance with an accuracy of 98%, along with precision, recall, and F1-Score of 98% each. The model successfully classifies spam and non-spam emails with minimal errors, making it an effective solution for filtering unwanted emails and preventing data breaches and phishing attacks. This research demonstrates that Logistic Regression, validated through experimental analysis, is a reliable and efficient method for spam email classification and can be applied in real-world email filtering systems.
Perancangan sistem E-Learning berbasis website untuk mendukung pembelajaran di SDN kalaki Wahyuni, Wenti Ayu; Fahrurrazikin, Fahrurrazikin; Zaeniah, Zaeniah
Journal Software, Hardware and Information Technology Vol 5 No 1 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i1.163

Abstract

Technology plays a significant role in enhancing the quality of education, especially in remote areas such as SDN Kalaki. This school faces challenges like inadequate infrastructure and distractions from non-academic activities, such as horse racing, which divert students' focus from education. This study aims to design and develop a web-based e-learning information system accessible to students and teachers from various locations. The system is designed to improve teacher-student interaction and increase learning interest. The research employs a prototyping approach, involving interviews, observations, and iterative development. The results indicate that the developed e-learning system effectively enhances the efficiency of the learning process and provides a relevant solution to the limitations of educational access in the region.
Pengujian Sistem Identitas Digital Siswa Berbasis Blockchain untuk Keamanan dan Transparansi Menggunakan Black-Box Testing Slam, Berta Erwin; Irawan, Feri; Efranda, Nolan; Herikson , Rifaldi
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.182

Abstract

This study proposes a blockchain-based student identity card system as a secure and transparent solution for managing digital identities at the senior high school level. The system is designed to address various issues found in conventional identification methods, such as data forgery, physical card damage, and limited verification capabilities. Developed using the Laravel framework and integrated with a private blockchain network, student identity data is hashed and stored permanently. The digital identity is represented as a QR code printed on the student ID card. Identity verification can be performed independently through a web-based application without relying on a central authority. System evaluation was conducted through functional testing using the black-box testing method, as well as user testing to assess reliability, efficiency, and usability. Test results showed that the system was able to verify identities with an average QR code response time of less than 3 seconds and a login success rate of 98%. Therefore, the integration of blockchain technology into the web platform proves to be an innovative and feasible approach to modernizing student identity management. This system contributes to strengthening the digital transformation of secondary education in a secure, efficient, and decentralized manner.
Penerapan Natural Language Processing (NLP) dengan Metode Cosine Similarity pada Sistem E-Monev untuk Pencarian Program Pembangunan Daerah Ansyori, Moch Faizal; Ahmad Heru Mujianto
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.183

Abstract

The Evaluation and Monitoring System (E-Monev) is a digital tool utilized for the periodic oversight of performance achievements and budget absorption within Regional Government Organizations (OPD). A primary challenge in its implementation lies in accurately identifying relevant regional development programs based on user text input. Conventional keyword-based search approaches are limited in their ability to comprehensively understand the semantic meaning of text, frequently yielding inaccurate or contextually irrelevant results. This study aims to design and develop a semantic search feature for the E-Monev system. It applies a Natural Language Processing (NLP) approach, employing the Cosine Similarity method and text representation based on BERT (Bidirectional Encoder Representations from Transformers). The data for this research was sourced from the Work Plan (Renja) documents of all OPDs in Bojonegoro Regency. These Renja documents were prepared in accordance with the Decree of the Minister of Home Affairs Number 900.1.15.5-3406 of 2024, which constitutes the Second Amendment to Decree of the Minister of Home Affairs Number 050-5889 of 2021, regulating the verification, validation, and inventory of updates to the classification, codification, and nomenclature of regional development and financial planning. During the analysis, both user input texts and Renja data were converted into embedding vectors, from which their semantic similarity was calculated. Test results demonstrate that the developed system significantly improves search accuracy, achieving a precision value of 0.884. 
Perancangan Sistem Mobile Reporting Berbasis Android untuk Pelaporan Preventive dan Corrective Maintenance di PT Telkom Indonesia Divisi Regional V Andre Saputra, Nova; Hari, Yulius; Darmanto, Darmanto
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.191

Abstract

Preventive, corrective, and maintenance activities are crucial tasks in the NE & OM Division of PT Telkom Indonesia Regional V. Despite the availability of a reporting system through Telegram bot and PACMAN-NEO web, the recording and reporting process is still considered less than optimal and is often complained about at the regional level. The main problems include data inaccuracy, lack of innovation and credibility, and visualization that has not met user expectations. This research aims to overcome the problems of slow, inaccurate, and less real-time reporting, as well as improve the features of navigation, verification, and monitoring of technician activities. The development method used is the System Development Life Cycle (SDLC) Waterfall model. The application was developed using Dart and Flutter as front-end frameworks, PHP and Laravel for API management, and PostgreSQL as the database. The black box test results show that all features function according to user needs. This application has been tested by 3 regional managers, 4 witel managers, 1 regional admin, and 10 technicians. User evaluation showed results of 75% for usability, 63% for ease of use, and 78% for user satisfaction. These results indicate that the developed application is feasible to use as a replacement for the previous Telegram-based PACMAN-NEO system.
Pengembangan Landing Page untuk Mendukung Digitalisasi PT Kosa Group Indonesia Menggunakan Platform Low-Code Irawan, Arswenda Jameci; Marzal, Jefri; Fadhila Putri, Mutia
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.196

Abstract

This study aims to develop a landing page as part of the digitalization strategy of PT Kosa Group Indonesia, a culinary company consisting of three main divisions: Kosarasa, Kosa Team, and Risafood. The development followed the waterfall method through observation, literature review, system design, implementation, and testing. The Framer platform was selected as a low-code solution due to its efficiency in building responsive, maintainable interfaces without the need for complex programming. This initiative was driven by the limitations of previously used platforms such as LinkTree, which lacked the capability for visual customization and did not effectively unify multi-division information. Performance testing using Google PageSpeed Insights showed an increase from 44 to 54 on mobile and from 44 to 55 on desktop after visual and structural optimizations. Usability testing using the System Usability Scale (SUS) yielded an average score of 87.25, which is categorized as excellent. The results indicate that low-code-based landing page development offers an effective solution to support business digitalization in the culinary sector, while maintaining development efficiency, strong visual identity, and user-friendly experience.
Perbandingan Kinerja Model Pembelajaran Mesin Random Forest dan K-Nearest Neighbor (KNN) untuk Prediksi Risiko Kredit pada Layanan Pinjaman Online Prayudani, Santi; Sibarani, Yous; Salam, Azrizal; Lubis, Arif Ridho
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.204

Abstract

This study aims to compare the performance of two popular machine learning algorithms, Random Forest and K-Nearest Neighbor (KNN), in predicting creditworthiness in online lending systems. The research uses the publicly available Loan Approval Prediction Dataset from Kaggle, which contains borrower profiles such as employment status, number of dependents, annual income, loan amount, loan term, and credit score. Data preprocessing included cleaning, handling missing values, outlier removal, and transformation through normalization and encoding. The dataset was divided into 80% training data and 20% testing data. Random Forest was configured with 100 decision trees and unlimited depth, while KNN used an optimal k value of 5 determined by grid search. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results showed that Random Forest outperformed KNN with consistently higher values (97%) across all metrics, demonstrating strong stability and superior pattern recognition capabilities. KNN, with an accuracy of 89%, still showed good performance and can be considered a lightweight alternative for simpler applications.
Analisa Pola Penyebaran Pengguna Layanan Transjakarta dengan Metode K-Means Clustering Reynaldi , Reynaldi; Faiz Djarot, Raihan Jamal; Wahyudi, Mochamad; Sumanto , Sumanto; Budiman, Ade Surya
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.205

Abstract

This study analyzes the spatial distribution patterns of Transjakarta service users in Jakarta using the K-Means Clustering algorithm. The dataset, obtained from the Kaggle platform, consists of 189,501 passenger transaction records, including tap-in and tap-out locations, travel times, and user-related information. The research process involves data collection, preprocessing to remove missing values, application of the K-Means Clustering algorithm, and determination of the optimal number of clusters using the elbow method. Based on the analysis, the optimal number of clusters is identified as four (K=4). A scatter plot visualization presents user distribution patterns based on geographic coordinates and service usage times. Each cluster represents a group of users with similar travel characteristics. This analysis results in a segmentation that reflects variations in Transjakarta passenger mobility patterns and illustrates how travel activity is distributed across spatial and temporal dimensions within the urban area of Jakarta.
Penerapan Machine Learning untuk Klasifikasi Teks Depresi pada Kesehatan Mental dengan SVM, TF-IDF, dan Chi-Square Ridha, Muhammad; Abdur Rohman, Muhammad Kholil; Agustin, Dian; Shaputra, Deni Handika; Manayla, Yasmine; Malikah, Amiroh Hanan
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.210

Abstract

Mental health has become a crucial global issue, with increasing numbers of individuals expressing their psychological conditions openly on social media platforms. This study aims to classify tweets related to mental health, specifically depression, using a combination of Support Vector Machine (SVM), Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction, and Chi-Square feature selection techniques. Although this approach has been widely applied in domains such as product and movie reviews, its application in the mental health context remains limited. The main challenge lies in capturing implicit psychological nuances and indirect expressions frequently present in platforms like Twitter, unlike the explicit text in other domains. Moreover, most prior studies have not integrated comprehensive preprocessing stages including lemmatization, stopword removal, and duplicate elimination for mental health data on social media. This research employs a dataset of 26,448 tweets derived from Kaggle and self-crawled data. The best result was achieved using an SVM with an RBF kernel without Chi-Square feature selection, yielding an accuracy of 74.93%. The study demonstrates that a comprehensive preprocessing pipeline can enhance classification performance. However, the model still struggles with sarcastic or ironic contexts. Future research is recommended to adopt deep learning approaches such as BERT or LSTM to capture more complex textual contexts.
Deteksi Penyakit pada Daun Tomat Menggunakan Kombinasi Ekstraksi Fitur Colors Moments dan Grey Level Co-Occurrence Matrix (GLCM) Syarif , Ririn Suharni; Akbar , Muhammad Nur; Darmatasia, Darmatasia
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.214

Abstract

Tomato is one of the leading horticultural crops widely cultivated by farmers in Indonesia. In addition to its high economic value, tomatoes are rich in nutrients beneficial to human health, such as vitamin C, lycopene, and other antioxidants. However, tomato productivity is highly vulnerable to decline due to various diseases, particularly those affecting the leaves. These diseases not only reduce the quality of the harvest but also significantly threaten production quantity. Therefore, early detection of leaf diseases in tomato plants is essential to help farmers, especially novice farmers, take timely and appropriate treatment actions. This study aims to develop a digital image-based detection system for tomato leaf diseases using feature extraction methods and classification algorithms. In the image pre-processing and feature extraction stages, the Color Moments algorithm is used to capture color information, while the Gray Level Co-occurrence Matrix (GLCM) represents leaf texture. The classification process is carried out using the Random Forest algorithm. The dataset used in this study was obtained from Kaggle, consisting of 5,451 images of tomato leaves categorized into six classes: Leaf Spot, Leaf Mold, Septoria Leaf Spot, Mosaic Virus, Bacterial Spot, and Healthy Leaf. Test results show that the developed model achieved an accuracy of 90%. These findings indicate that the system can detect tomato leaf diseases with a relatively high level of accuracy. The system is expected to assist farmers, especially beginners, in identifying plant diseases more quickly and accurately, thereby improving treatment efficiency and increasing crop yields.

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