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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL DESTINASI PARIWISATA JOIV : International Journal on Informatics Visualization Konvergensi : Jurnal Ilmiah Ilmu Komunikasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ulul Albab ILKOM Jurnal Ilmiah JURNAL PENDIDIKAN TAMBUSAI JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Journal of Humanities and Social Studies AL-ULUM: JURNAL SAINS DAN TEKNOLOGI Jurnal Teknologi Terpadu Jurnal Review Pendidikan dan Pengajaran (JRPP) Pantun: Jurnal Ilmiah Seni Budaya Jurnal Informasi dan Teknologi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Sosial Humaniora Sigli Jurnal Scientia Journal of System and Computer Engineering Gunahumas Jurnal Ilmiah Wahana Pendidikan Jurnal Informatika Terpadu Indonesian Journal of Intellectual Publication (IJI Publication) Edu Cendikia: Jurnal Ilmiah Kependidikan Global Abdimas: Jurnal Pengabdian Masyarakat Sci-Tech Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi Sentra Dedikasi: Jurnal Pengabdian Masyarakat Journal Pharmacy and Application of Computer Sciences Jurnal Informatika: Jurnal Pengembangan IT Jurnal Kajian Pendidikan dan Psikologi Journal of Innovative and Creativity Toplama Jurnal Pembelajaran Bahasa dan Sastra Advances in Computer System Innovation Journal (ACSI Journal) PESHUM Indonesian Journal of Intellectual Publication (IJI Publication)
Firman Aziz
Universitas Pancasakti Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : Journal of System and Computer Engineering

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Recognition of Human Activities via SSAE Algorithm: Implementing Stacked Sparse Autoencoder Batau, Radus; Kurniyan Sari, Sri; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1470

Abstract

This study evaluates the performance of Stacked Sparse Autoencoder (SSAE) combined with Support Vector Machine (SVM) against a standard SVM for classification tasks. We assessed both models using accuracy, precision, sensitivity, and F1 score. The SSAE Support Vector Machine significantly outperformed the standard SVM, achieving an accuracy of 89% compared to 37%. SSAE also achieved higher precision (87% vs. 75%) and sensitivity (89% vs. 37%), with an F1 score of 88% versus 36% for the standard SVM. These results indicate that SSAE enhances the model’s ability to capture complex patterns and provide reliable predictions. This study highlights the effectiveness of SSAE in improving classification performance, suggesting further research with larger datasets and additional optimization techniques to maximize model efficiency
ARIMA Method Implementation for Electricity Demand Forecasting with MAPE Evaluation Wungo, Supriyadi La; Aziz, Firman; Jeffry, Jeffry; Mardewi, Mardewi; Syam, Rahmat Fuady; Nasruddin, Nasruddin
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1666

Abstract

Electricity demand forecasting is critical for efficient energy management and planning. This study focuses on the development and implementation of the Autoregressive Integrated Moving Average (ARIMA) method for forecasting electricity demand in South Sulawesi's power system. The evaluation of forecasting accuracy was conducted using the Mean Absolute Percentage Error (MAPE), which measures the percentage error between predicted and actual values. Two experiments were conducted with different ARIMA models: ARIMA(5,1,0) and ARIMA(2,0,1). Results showed that the ARIMA(5,1,0) model achieved a MAPE of 2.15%, while the ARIMA(2,0,1) model performed slightly better with a MAPE of 1.91%, indicating highly accurate predictions. The findings highlight the effectiveness of the ARIMA method in forecasting electricity demand, providing a reliable tool for energy providers to optimize resource allocation and enhance operational efficiency. Future research may explore integrating ARIMA with other advanced methods to further improve forecasting performance.
Detection of Persistent vs Non-Persistent Medications in Pharmacy Using Artificial Intelligence: Development of Intelligent Algorithms for Pharmaceutical Product Safety Abasa, Sustrin; Aziz, Firman; Ishak, Pertiwi; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1618

Abstract

The pharmaceutical industry requires an effective system to detect medications that are persistent and non-persistent, in order to improve safety and the efficiency of product management. This study aims to develop a system based on Artificial Intelligence (AI) using the Decision Tree algorithm to classify medications based on prescription data provided by doctors. The dataset used in this study includes prescription information, such as medication type, prescription quantity, frequency of use, and duration of medication use, which are used to determine whether the medication is persistent or non-persistent. The Decision Tree algorithm is applied to develop a reliable classification model, with the goal of detecting medications that are used continuously (persistent) and those that are not used on a continuous basis (non-persistent). This study applies AI technology in the pharmaceutical field, focusing on the use of doctor prescriptions and classifying medications based on usage characteristics. The results of the study show that the algorithm performs well with an accuracy of 78.33%, recall of 0.7804, precision of 0.7804, and an F1 score of 0.6934, indicating the model's ability to classify medications with reasonable accuracy.
Classification of Chocolate Consumption Using Support Vector Machine Algorithm Aziz, Firman; Jeffry, Jeffry; Ayu Asrhi, Nur; La Wungo, Supriyadi
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1860

Abstract

Chocolate, derived from the processing of cocoa beans (Theobroma cacao), is a widely consumed product with potential health risks when consumed excessively. This study investigates the classification of chocolate consumption behaviors using the Support Vector Machine (SVM) algorithm and evaluates its classification performance. A benchmark dataset on chocolate consumption was employed, partitioned into nine folds for training and testing purposes. To mitigate issues related to data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The experimental findings indicate that SVM, enhanced by SMOTE, demonstrates a reliable capacity for classifying chocolate consumption categories. Performance evaluation across multiple experiments revealed variations in Accuracy, Precision, Recall, and F1-Score, with overall accuracies ranging from 50% to 60%, suggesting moderate but consistent classification performance.
Performance Exploration of Tree-Based Ensemble Classifiers for Liver Cirrhosis: Integrating Boosting, Bagging, and RUS Techniques Aziz, Firman; Jeffry, Jeffry; Wungo, Supriyadi La; Rijal, Muhammad; Usman, Syahrul
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2031

Abstract

Liver cirrhosis, as a significant chronic liver disease, exhibits a rising global prevalence, demanding more effective preventive approaches. In an effort to enhance early detection and patient management, this research proposes the development of a liver cirrhosis risk prediction model using machine learning technology, specifically comparing the performance of three ensemble tree models: Ensemble Boosted Tree, Ensemble Bagged Tree, and Ensemble RUSBoosted Tree. Utilizing clinical and laboratory data from adults with a history or risk of cirrhosis, the study reveals that Ensemble Bagged Tree achieved the highest accuracy at 71%, followed by Ensemble Boosted Tree (67.2%) and Ensemble RUSBoosted Tree (66%). Analysis of clinical and laboratory variables provides further insights into the most significant contributors to risk prediction. The findings lay the groundwork for the advancement of a more sophisticated liver cirrhosis risk prediction tool, supporting a vision of more personalized and effective preventive strategies in liver disease management
A Deep Learning Approach to Respiratory Disease Classification Using Lung Sound Visualization for Telemedicine Applications Wahyudi, Andi Enal; Batau, Radus; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2144

Abstract

This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.
Enhancing Human Activity Recognition with Attention-Based Stacked Sparse Autoencoders Batau, Radus; Sari, Sri Kurniyan; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2148

Abstract

This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.
Sistem Manajemen Penjadwalan Pengajaran Dosen berbasis SMS Gateway jeffry, jeffry; Velayaty, Ali Akbar; Aziz, Firman
Journal of System and Computer Engineering Vol 4 No 2 (2023): JSCE: Juli 2023
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v4i2.648

Abstract

To improve performance in teaching time, one of which is by being punctual in teaching, therefore a system is needed to remind lecturers when teaching time arrives. Along with the development of technology, almost everyone has a communication device called a cell phone, one of the functions that are often used is sending messages or SMS. SMS Gateway is a platform that can be used to send and receive SMS whose settings can be made using PHP with data storage tools in the form of MySQL. Reminder SMS and teaching schedule monitoring using the SMS Gateway is a system used to remind lecturers about class schedules via SMS that was developed using the PHP programming language.
Sistem Monitoring Status Meja Pada Restoran Berbasis Internet of Things (IOT) Mardewi, Mardewi; Iskandar, Imran; Sofyan, Sofyan; La Wungo, Supriyadi; Aziz, Firman
Journal of System and Computer Engineering Vol 4 No 2 (2023): JSCE: Juli 2023
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v4i2.816

Abstract

The condition of a busy restaurant sometimes makes it difficult for waiters to monitor and provide satisfactory service to customers. Customers must wave when they want to call waiters but feel ignored or not seen and will feel uncomfortable with the atmosphere and calm in the dining room. An Internet of Things (IOT) based desk status monitoring system is a concept that has the ability to transfer data over a network without the need for human-to-human interaction. This study proposes a table status monitoring system in IOT-based restaurants. This tool is made so that it can be applied to large rooms and crowded visitors. A device equipped with wireless communication to send data to the server, so that it can be monitored in real time. If the button on the tool is pressed, the system will send a signal to the relay to give a call sign warning to restaurant staff/waitresses. The results of this study indicate that status information from tables requesting service from waiters will respond to these service requests and help optimize service at restaurants so that they can satisfy their customers.
Co-Authors Abasa, Sustrin Achmad Hufad Adriana, Andi Nur Ilmi Adriana, Andi Nurilmi Afifah, Mira Aulia Ahmad Sukarna Syahrir, Ahmad Sukarna Akbar Taufik Almuhajir Haris, Almuhajir Amalyah, Aam Amelia, Kiki Resqy Ampauleng Ampauleng Andi Nurilmi Adriana Andi Taufiqurrahman Akbar Andjani, Andita Dwi Andri Kurniawan Andyka Andyka, Andyka Anirwan Anirwan Annisa Sakanti Tamir Anugriaty Indah Asmarany Arafah, Muhammad Nur Areta Nararya Putri Setiadi Arifin, Syaadiah Armansyah, M Rezky Armin Lawi Arni, Sitti Artikasari, Devina Arvito, Djendral Muhammad Aulia, Khansa Ayu Asrhi, Nur Ayu, Rizkia Siva Aziz, Naufal Nuurul Aziz Azizah, Regita Nur Azminuddin I. S. Azis Barokah, Nurul Nur Batau, Radus Buang, Ariyani Buang, Misbahuddin Buyung Firmansyah Delilah, Eva Dessy Putri Wahyuningtyas Dhilan Sasmita Eko Nur Hermansyah Enal Wahyudi, Abdi Fadhila Amri, Nur Faisal Rahman Fajriana, Fajriana Fani Temarwut, Farid Fatimah Azzahra NF Ferdiana, Ryan Fiina Lanahdiyan Najah Firmansyah Firmansyah Firmansyah Firmansyah, Arya Pramudya Fuadi Syam, Rahmat Fujiono, Fujiono Gunawan, Resky Nuralisa H, Rezha Ilma Hafsah, Hafni Hamdani Nur, Nur Hanayanti, Citra Siwi Hanum Nur Alifia Hasriani Hasriani, Hasriani Hayati, Ristia Nur Hechmi SHILI Hikam, Zaki Maula Hilyah, Finan Azka Nuzilla Indrayani, Lilis Intan, Dyah Noor Irmawati Irmawati Ishak, Pertiwi Iskandar, Imran Ismail Ismail Istiqamah, Nurul Jafar Jafar Jafar Jafar Jeffry Jeffry Jeffry Kahar Gani Khairunnisa, Salwa Khurosani, Bilqhis Isywal Kurniyan Sari, Sri Kusumawardhani, Anggun L.E.P, Benny La Wungo, Supriyadi Lempi, Herga Andar Lutfi Budi Ilmawan, Lutfi Budi M Rezky Armansyah Manan, Linda Ifni Pratiwi Marcelina, Dona Mardewi Mardewi Mardewi, Mardewi Marzuki Maulani, Rista Nabilah Meiliana, Annisa Merdewiningsi, Andi Mindra, Davin Septian Misbah Abdul Aziz Muhammad Arfah Asis Muhammad Lutfi Muhammad Rijal Muhammad Rijal Mutia Maulida Nasir, Norma Nasruddin Nasruddin Nur Ayu Asrhi Nur Ayu Asrhi Nur Hamadani Nur Nur Hamdani Nur Nur, Nur Hamdani Nurafni Shahnyb Nurafni Shahnyb Nurdyansa Nurul Fathanah Mustamin Nurul Istiqamah Osman, Isnawati Panggabean, Benny Leonard Enrico Paramitha, Aura Rahma Priambodo, Caka Gatot Putra, Sudarmadi Putri Ayu Lestari Qamal Qamal raharjo, itot bian Rahma, Nabila Nailatur Rahma, Widya Rahmania Nur Saputra Reinata, Vanya Fara Restu Arsyana Riesna Apramilda Rijal, Muhammad Rizqya Aufa Nuraini Rofi’i, Agus Rohmah Nur Hidayah Ronald Yehezkiel Sitompul Rozak, Rama Wijaya Abdul Ryan Ferdiana Sari, Sri Kurniyan Satar Satar Sembiring, Darmawanta Shahnyb, Nurafni Shavi Khalwa Khalisha Simarmata, Victoria Clareva Siti Saidah Soeriakartalegawa, Aldo Pranata Sofyan Sofyan Sri Purwati, Sri Sumardi . Sumardi Sumardi Suroso Suroso Syahrul Usman Syam, Rahmat Fuadi Syam, Rahmat Fuady Tanniewa, Adam M Taufik , Akbar Taufik, Akbar Tazkillah, Ghina Ajmal Triani, Novita Trianita, Desi Umar, Hendra Usulu, Elvira M. Velayaty, Ali Akbar Vismania S. Damaianti, Vismania S. Wahab, Andyka Wahyudi, Andi Enal Wiftasya, Najla Wijaya, Neti Septi Wulandari, Ayu Ratna Wungo, Supriyadi La Yahya, Kurnia Yance Manoppo Yarkuran, Nuru Zahra Hasna Nabilla Zahra, Agifa Faiza Zevi, Fidiya Iryana Zhafira Tsania Rasyiffah Zulkarnain Zulkarnain Zulkarnain Zulkarnain