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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal INSYPRO (Information System and Processing) JPPI (Jurnal Pendidikan Islam Pendekatan Interdisipliner) ComTech: Computer, Mathematics and Engineering Applications Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Journal of Intelligent Computing and Health Informatics (JICHI) Journal Peqguruang: Conference Series
Akhmad Qashlim, Akhmad
Universitas Al Asyariah Mandar Sulawesi Barat
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Perbandingan Algoritma Naive Bayes dan K-Means untuk menentukan Status Gizi Stanting Rahman, Ariastuti; Qashlim, Akhmad; Nurmarifah
Jurnal INSYPRO (Information System and Processing) Vol 10 No 1 (2025)
Publisher : Prodi Sistem Informasi UIN Alauddin

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Abstract

Stunting is a condition where children under five years of age suffer from long-term nutritional deficiencies and cause growth and development disorders, increasing morbidity and mortality. Handling the nutritional status of stunting in Indonesia, especially in Polewali Mandar Regency, is important considering that stunting cases in toddlers are a serious problem. To assist health workers in determining the nutritional status of stunting, an appropriate and effective method is needed. This study will provide an alternative way to determine the nutritional status of stunting using Data mining techniques, namely the Naive Bayes and K-Means methods with anthropometric indicators. There are 173 toddler data processed. The results of the study show that the combination of the two methods for the accuracy value of the Naive Bayes algorithm has a value of 89% while K-Mens has a value of 54%. By using the precision parameter, the Naive Bayes algorithm has a value of 84% while K-Mens has a value of 58%. By using the recall parameter, the Naive Bayes algorithm has a value of 97% while K-Mens has a value of 60%. Based on the values obtained, it can be concluded that the Naive Bayes algorithm has higher accuracy, precision and recall when compared to the K-Means algorithm..
Sistem Penggunaan Air Bersih Ke Rumah Masyarakat Berbasis Internet of Things (IoT) Swarno, Sugen; Qashlim, Akhmad; Rahman, Ariastuti
JURNAL ILMU KOMPUTER Vol 10 No 2 (2024): Edisi September
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v10i2.306

Abstract

The uncontrolled distribution of clean water to people's homes can lead to excessive use by some communities, resulting in the local government often checking directly at each house. This makes officers very overwhelmed every time they check the location, because there is no data or records that can be used as evidence to find out how much water is used by each community's house. So one solution that can be done by keeping up with the times is to create a system that can control the use of clean water. The desired result is to make it easier for the government to check the use of clean water only by using tools commonly used in everyday life such as smartphones or laptops.
SISTEM MONITORING KECEPATAN DAN ARAH ANGIN BERBASIS INTERNET OF THINGS (IOT) SEBAGAI PERINGATAN DINI BENCANA ALAM Rahman, Ariastuti; Qashlim, Akhmad; Tamin, Rosmawati; Syam, Muhammad Farid
JURNAL ILMU KOMPUTER Vol 10 No 1 (2024): Edisi April
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v10i1.310

Abstract

A tornado is a form of extreme weather caused by differences in air pressure and temperature in the atmosphere. This phenomenon can cause loss of life and psychological disorders. To overcome this serious impact, a system that is able to monitor the condition of the surrounding environment in real-time and provide early warning is needed. This research aims to design an Internet of Things (IoT) system for real-time monitoring of wind speed and direction as well as environmental conditions that is effective for providing early warning of natural disasters. Testing and calibration methods were carried out by comparing IoT devices with standard tools. Additional tests were conducted on the anemometer to evaluate the consistency of wind speed at certain heights and at locations adjacent to the coast and some distance from the coast. The results of this IoT device design demonstrate its ability to detect temperature, humidity, air pressure, wind speed, and wind direction, and send real-time data to the monitoring system. This research confirms that the web-based monitoring system is effective in monitoring environmental conditions and sending early warnings when the system detects tornado characteristics.
CLUSTERING NILAI ENGLISH SUNSET MAHASISWA MANGGUNAKAN METODE K-MEANS PADA LEMBAGA BAHASA DAN PENGEMBANGAN KARAKTER (LBPK) UNASMAN salmawati, Salmawati; Rendi, Rendi; Qashlim, Akhmad
JURNAL ILMU KOMPUTER Vol 10 No 1 (2024): Edisi April
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v10i1.312

Abstract

The UNASMAN Language and Character Development Institute (LBPK) is used by all UNASMAN students to improve their English skills so they can continue their studies abroad and as alumni can later compete in the world of work with other alumni both nationally and internationally. This research aims to group student scores in the English Sunset program at the Unasman Language and Character Development Institute (LBPK) using the K-Means method. The K-Means method was chosen because of its effective ability to group data based on similarity of attributes, making it possible to identify groups of students with similar value characteristics. Student score data is collected, processed and analyzed using the K-Means algorithm to determine the optimal number of clusters. The research results show that students can be grouped into three main clusters: students with high scores, medium scores, and low scores. This information provides valuable insight for LBPK in designing more targeted teaching strategies and providing additional support for student groups in need. Feasibility analysis from a technological and operational perspective shows that this system can be implemented with adequate infrastructure and sufficient training support for staff and lecturers. This research confirms that the K-Means method can be used effectively to improve the qualitys of learning at LBPK Unasman.
Co-Authors Andani Achmad Andi, Riswandi Arda, Abdul Latif Ashabul Kahpi Assiddiq, Muhammad Assidiq, Muhammad B, Basri Basri Basri Daniel Happy Putra Fatmawati Fatmawati Harlianti, Harlianti Hasruddin. B Khairat, UL M. Yogi Riyantama Isjoni Mawar Mawar, Mawar Muhammad Irsyad Muhammad Sarjan Nafsin, Muhammad Nurdiansah, Nurdiansah Nurmarifah Ochtavia, Gustia Paramita, Paramita Rachmat Gernowo Rahmadani Rahmadani Rahman, Ariastuti Rendi Rendi, Rendi Sahrul, Muh Sairah, St. Salmawati Salmawati, Salmawati Sopyan, Edy sukarni, andi Swarno, Sugen Syam, Muhammad Farid Syarli, Syarli Tamin, Rosmawati Toni Prahasto Zulkifli, Azis