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All Journal International Journal of Electrical and Computer Engineering Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Almana : Jurnal Manajemen dan Bisnis AL-ULUM: JURNAL SAINS DAN TEKNOLOGI Jurnal Teknologi Terpadu SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Journal of System and Computer Engineering Jurnal Informatika Terpadu Indonesian Journal of Intellectual Publication (IJI Publication) Global Abdimas: Jurnal Pengabdian Masyarakat Publikasi Riset Mahasiswa Manajemen Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Journal Pharmacy and Application of Computer Sciences Journal of Innovative and Creativity Advances in Computer System Innovation Journal (ACSI Journal) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Indonesian Journal of Intellectual Publication (IJI Publication)
Jeffry Jeffry
Prodi Ilmu Komputer, Institut Teknologi Bacharuddin Jusuf Habibie
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Analisis Perilaku Pelanggan menggunakan Metode Ensemble Logistic Regression jeffry -; Syahrul Usman; Firman Aziz
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 6 No. 2 (2023): Jutikomp Volume 6 Nomor 2 Oktober 2023
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v6i2.4258

Abstract

Customer behavior analysis is crucial for companies, especially Small and Medium Enterprises (SMEs), to make strategic decisions. Through customer behavior analysis, patterns of customer behavior in purchasing a product or service can be identified. This provides insights into preferences and the right strategies to maintain or increase customer loyalty. This research aims to analyze customer behavior using the ensemble logistic regression method. Data was collected from the company's customer database over the last 5 years. Customer behavior is characterized by gender, age, and the preferred vehicle transmission type. The ensemble logistic regression method was implemented to improve model accuracy. The results show that the ensemble technique can enhance the accuracy of the Logistic Regression method. Model accuracy significantly increased with boosting, achieving an accuracy of 76%, while the model with bagging achieved an accuracy of 75%.
PENINGKATAN KOMPETENSI GURU MELALUI IMPLEMENTASI E-ASSESSMENT PADA DINAS PENDIDIKAN KABUPATEN BONE Jeffry, Jeffry; Usman, Syahrul; Aziz, Firman; Anirwan, Anirwan; Sumardi, Sumardi; Ismail, Ismail; Qamal, Qamal; Haris, Almuhajir; Gani, Kahar; Syam, Rahmat Fuadi
GLOBAL ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2024): Mei 2024, GLOBAL ABDIMAS
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/globalabdimas.v4i1.496

Abstract

Penilaian yang efektif merupakan elemen penting dalam meningkatkan mutu pendidikan. Dalam era digital yang terus berkembang, penggunaan teknologi informasi dalam evaluasi pembelajaran telah membawa dampak positif. Salah satu inovasi yang menjanjikan adalah penggunaan E-Assessment, yaitu evaluasi yang dilakukan secara elektronik. Dalam konteks Dinas Pendidikan Kabupaten Bone, pengabdian ini bertujuan untuk meningkatkan kompetensi guru-guru sekolah dasar melalui implementasi E-Assessment. Metode pengabdian ini melibatkan pelatihan dan pendampingan bagi guru-guru dalam penerapan E-Assessment sebagai alat evaluasi pembelajaran. Selain itu, pengabdian juga melibatkan pengembangan modul dan panduan praktis yang menggambarkan langkah-langkah implementasi E-Assessment yang efektif. Pendekatan kolaboratif dan partisipatif digunakan untuk memastikan keterlibatan guru-guru dalam pengembangan dan implementasi E-Assessment. Hasil pengabdian ini menunjukkan peningkatan pemahaman guru meningkat 28%, pengetahuan konsep e-assessment 47%, relevansi e-assessement dalam konteks pendidikan meningkat 88%, dan pengetahuan tentang dampak penggunaan e-assessment meningkat 4%.
Pengembangan Absensi berbasis Mobile Aplikasi pada Badan Kepegawaian dan Pengembangan Sumber Daya Manusia Kabupaten Bone Usman, Syahrul; Jeffry, Jeffry; Aziz, Firman
Jurnal Teknologi Terpadu Vol 7 No 2: Desember, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i2.437

Abstract

Since being designated a global pandemic by the world health agency (WHO), the Corona Virus Disease (Covid-19) outbreak has become a scourge worldwide; various standard transmission control procedures have been set by WHO to break the chain of transmission.  Bone District Government through the Circular of the Regional Secretary No. 800/1919/VI/BKPSDM/2020 dated June 4, 2020, regarding the work system of State Civil Apparatus Employees (ASN) in the new standard order regulates employee attendance using manual attendance and not using fingerprint attendance machines, and this will undoubtedly affect the recording of the performance of each ASN where the attendance data is already connected to the e-performance application that is applied to the Bone district. The purpose of this research is to create an online attendance application based on Android Mobile to be an alternative way of being absent by using the data communication method using the Representational State Transfer (Rest) web service architecture and utilizing the HTTP protocol with JavaScript Object Notation (JSON) format and the Java programming language as a language. Mobile Application programming. The results of this study are a mobile-based attendance application that has been tested for web service performance using the Apache JMeter application to ensure this application is ready to be used simultaneously by many ASN.
TRANSFORMASI DIGITAL DALAM INDUSTRI KERAJINAN RUMAHAN MELALUI PENGGUNAAN TEKNOLOGI MACHINE LEARNING Jeffry, Jeffry; Nurdyansa, Nurdyansa; Usman, Syahrul; Sasmita, Dhilan; Arafah, Muhammad Nur
JMM (Jurnal Masyarakat Mandiri) Vol 7, No 6 (2023): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v7i6.19209

Abstract

Abstrak: Salah satu masalah mendasar yang dihadapi oleh Industri Rumah Tangga adalah pengumpulan dan pengolahan data yang masih dilakukan secara konvensional atau manual. Dalam dunia bisnis modern, analisis data adalah kunci untuk memahami tren penjualan dan preferensi pelanggan. Pengabdian kepada masyarakat ini bertujuan untuk mengatasi masalah ketidaktersediaan teknologi dalam usaha sablon "Kamar Gelap," yang menghambat analisis data penjualan dengan memberikan pemahaman kepada mitra tentang penggunaan digital marketing dan machine learning. Pendekatan pengabdian melibatkan identifikasi kebutuhan mitra, perencanaan program, pelaksanaan kegiatan, dan evaluasi hasil. Metode yang digunakan pada kegiatan ini adalah metode sosialisasi dan pelatihan tentang digital marketing dan machine learning. Usaha "Kamar Gelap Screen Printing" menjadi mitra dalam pengabdian ini, dengan partisipasi sebanyak 14 peserta. Evaluasi menunjukkan peningkatan signifikan dalam kemampuan analisis data, penguasaan teknologi informasi, dan strategi pemasaran peserta. Hasilnya meliputi pembuatan sistem informasi penjualan dan sistem prediksi penjualan berdasarkan data transaksi. Penilaian menunjukkan peningkatan pemahaman mitra: transformasi digital meningkat 29%, konsep machine learning naik 56%, relevansi teknologi machine learning meningkat 86%, dan pemahaman tentang dampak transformasi digital terhadap efisiensi produksi meningkat 7%.Abstract: One fundamental challenge faced by Household Industries is the conventional and manual collection and processing of data. In the modern business world, data analysis is key to understanding sales trends and customer preferences. This community engagement aims to address the issue of technology unavailability in the "Kamar Gelap" screen printing business, which hampers sales data analysis, by providing understanding to partners about digital marketing and machine learning usage. The approach involves identifying partner needs, program planning, activity implementation, and results evaluation. The methods employed in this initiative include socialization and training on digital marketing and machine learning. "Kamar Gelap Screen Printing" business is the partner in this engagement, with the participation of 14 attendees. The evaluation demonstrates a significant improvement in participants' data analysis skills, IT proficiency, and marketing strategies. The outcomes include the development of a sales information system and a sales prediction system based on transactional data. The assessment indicates an enhancement in partner understanding: a 29% increase in digital transformation comprehension, a 56% rise in machine learning concept awareness, an 86% increase in the relevance of machine learning technology, and a 7% improvement in understanding the impact of digital transformation on production efficiency.
Rancang Bangun Sistem Cerdas Pendeteksi Kerusakan Mesin Pada Sepeda Motor 4 Tak Aziz, Firman; Wungo, Supriyadi La; jeffry, Jeffry
Journal of System and Computer Engineering Vol 5 No 1 (2024): JSCE: Januari 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i1.1075

Abstract

The influence of using computer technology in solving a problem is very large, for example in determining motor damage. In this case, it is devoted to 4-stroke motorcycle vehicles. Currently, the average motorcyclist does not know about the symptoms or signs of damage to their vehicle, so motorists ignore these symptoms and consider these symptoms to be trivial problems, which will not cause damage to their motorcycles. Motorists will be aware of these symptoms after they can no longer use their vehicles. This research proposes to design an intelligent system that can help motorized vehicle drivers to find out earlier about symptoms or signs of damage to their vehicles and take appropriate action before more serious damage occurs to their vehicles. The results obtained are that an intelligent system can detect damage to a 4-stroke motor quickly and can facilitate the provision of solutions and the diagnostic process without a 4-stroke motor expert
Sistem Pendukung Keputusan Penentuan Destinasi Objek Wisata Dengan Metode Simple Additive Weighting (SAW) Berbasis Web jeffry, Jeffry; aziz, firman; usman, syahrul
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1339

Abstract

One of the biggest regional proceeds of the North Toraja Regency comes from the utilization of tourist objects as recreational objects whether for the local communities or the overseas. However, the lack of information and the lack of systems technology in Toraja destination caused many tourists to visited a few of the many tourism objects available. This problem causes tourists to tend to visit only a fraction of the many tourism objects. Based on these problems, we need a system that helps provide information and determine tourist objects suitable for each tourist, and the tour is more varied. This study produces a decision support system for selecting tourism objects in North Toraja using the “Simple Additive Weighting” method based on a website in the goal of assisting tourists to determine tourist place
Penerapan Metode Certainty Factor dan Forward Chaining pada Sistem Pakar Untuk Mendiagnosa Penyakit Ginjal Jeffry, Jeffry; Usman, Syahrul
Indonesian Journal of Intellectual Publication Vol. 1 No. 1 (2020): Nopember 2020, IJI Publication
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/ijipublication.v1i1.35

Abstract

Ilmu komputer yang mempelajari kemampuan komputer untuk bertindak dan memiliki kecerdasan seperti manusia dikenal sebagai kecerdasan buatan, yang termasuk dalam kecerdasan buatan antara lain: penglihatan komputer, pengolahan bahasa alami, robotika, jaringan syaraf tiruan, sistem pakar (expert system). Penelitian ini bertujuan untuk membuat suatu sistem pakar yang digunakan untuk mendiagnosa penyakit ginjal, dimana pengguna bisa mendiagnosis sendiri (skrining mandiri) berdasarkan gejala yang dirasakannya. Pengetahuan pada sistem direpresentasikan dalam bentuk aturan dan metode penalaran yang digunakan adalah metode runut maju (forward chaining) sedangkan nilai kepastian terhadap penyakit menggunakan metode certainty factor yaitu diperoleh dari kombinasi nilai dari user dan pakar. Hasil penelitian menunjukkan bahwa sistem ini mampu mendiagnosa kemungkinan jenis penyakit ginjal yang diderita oleh user dengan menampilkan besaran kepercayaan dari tiap-tiap penyakit. Dari hasil percobaan diperoleh bahwa nilai certainty factor pada Nefritis tubulointerstisial sebesar 0,7502, untuk Sistitis Interstisial sebesar 0,7308, Kanker Kandung Kemih sebesar 0,6429. Sehingga nilai CF terbesar merupakan keputusan dari sistem pakar ini. Besarnya nilai kepercayaan tersebut merupakan hasil perhitungan dengan menggunakan metode certainty factor.
Implementation of an Internet of Things (IoT)-Based Air Quality Monitoring System for Enhancing Indoor Environments Enal Wahyudi, Abdi; 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.v6i1.1466

Abstract

This research investigates the development and implementation of an IoT-based air quality monitoring system designed to improve indoor environmental conditions. The primary objective of this study is to develop a comprehensive system that continuously monitors air quality parameters, including smoke, LPG gas, carbon monoxide (CO), temperature, and humidity. The system integrates real-time data collection from various sensors, which is then processed and transmitted to a cloud platform for secure storage and detailed analysis. The user-friendly interface of the software allows for intuitive monitoring and reporting, while built-in notification and alert features ensure timely responses to significant air quality changes. Testing results demonstrate that the system operates with high reliability, providing accurate data and stable performance. The findings confirm that the system effectively addresses indoor air quality concerns and offers valuable insights for maintaining a healthy and safe environment. This research contributes to the field by showcasing a practical application of IoT technology in environmental monitoring.
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.
Co-Authors A Suyuti, Muh Zulfadli Abasa, Sustrin Abdullah, Khairal Adriani, Ika Reskiana Aljabar, Andi Almuhajir Haris, Almuhajir Andi Taufiqurrahman Akbar Andri Dwi Utomo Anirwan Anirwan Anwar Arafah, Muhammad Nur Ayu Asrhi, Nur Azminuddin I. S. Azis Batau, Radus Binti Mamluatul Karomah Buang, Misbahuddin Enal Wahyudi, Abdi Fadliana, Nurul Firman Aziz Ishak, Pertiwi Iskandar, Imran Ismail Ismail Ispanti Zaman Kahar Gani Kandakon, Elisabeth Tri Juliana Kurniyan Sari, Sri La Wungo, Supriyadi M Rezky Armansyah Manazil, Qornul Marcel Marcel Mardewi Mardewi Mardewi, Mardewi Mohd, Tengku Mon, Muhammad Donal Nasruddin Nasruddin Nipsa Asdiana Supak Nur Ayu Asrhi Nur Ayu Asrhi Nurdyansa Nursalim Nurul Ahyana Nyimas Artina Panggabean, Benny Leonard Enrico Putra Yuzi Bachmid, Fadel Muhammad Qamal Qamal Rijal, Muhammad Rimalia, Watty Santoso, , Budy Sari, Sri Kurniyan Sasmita, Dhilan Sentoso, Antony Sukarno Abby Sumardi Sumardi Susi Handayani Syafaat, Muhammad Syahrul Usman Syam, Rahmat Fuadi Syam, Rahmat Fuady Tarisafitri, Nahla Velayaty, Ali Akbar Wahyudi, Andi Enal Waluyo, Safril Rizki Wibowo. K, Trisno Wungo, Supriyadi La Yeti Kule Yunendar, Wakhid