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All Journal Jurnal Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal Inotera Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Eduscience (JES) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal AbdiMas Nusa Mandiri Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Minfo Polgan (JMP) Journal of Renewable Energy, Electrical, and Computer Engineering TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Informasi Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Jurnal Ilmu Komputer dan Sistem Informasi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Said Fadlan Anshari
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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IoT-Integrated Home Energy Management System with Real-Time Monitoring and Solar Panel Optimization Mhd Firza Ryzaaldy; Muhammad Fikry; Said Fadlan Anshari
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/micoms.v4i.949

Abstract

In this study, an IoT-integrated Home Energy Management System (HEMS) was developed using solar panels as the primary energy source. The system employs an ESP32 microcontroller as the core controller, equipped with DHT22, LDR, and INA219 sensors to monitor temperature, humidity, light intensity, voltage, and current. Real-time sensor data is presented on a web interface, allowing users to monitor system status and control devices like fans and lights either manually or automatically. The system demonstrated stable performance with a control response time of under one second and effective energy management aligned with environmental conditions. However, a key limitation was the limited capacity of the 10 Wp solar panel, particularly during low sunlight periods. To address this, enhancements such as improved load management or increased solar panel capacity are recommended. The system successfully implemented real-time monitoring and automated control, activating the fan at temperatures above 30 degrees Celsius and turning on lights when light intensity is below 1000 lux. This research highlights the potential of IoT technology in achieving efficient and sustainable home energy management.
Implementation of the Naïve Bayes Method in a Web-Based Fish Species Classification System Rizki Suwanda; Muhammad Fikry; Said Fadlan Anshari
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

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Abstract

The current fish resources are abundant, and the discovery of new species has increased the variety of fish in the ocean. These fish are categorized into three groups: demersal, pelagic, and reef fish, each with unique characteristics of their respective groups. The manual classification process for large datasets requires a long time and involves complex procedures. With the advent of data and information technology, it is now possible to recognize and identify several fish species found in the ocean, which can be classified into the three groups. To simplify this classification process, a web-based system has been developed to classify fish into these groups. The data to be processed in this research will be classified using the Naive Bayes method to address this issue. This technique utilizes large datasets to extract information that was previously unknown or inaccessible, and it can provide accurate information for various purposes. The data for this study will be collected from various internet references and direct data obtained from fish landing sites (TPI) in Lhokseumawe and North Aceh. Additionally, a literature review method will be used to complement the data analysis process. The development of the web-based system will be implemented to facilitate the classification of fish species based on the existing data.
The Influence of Google Lens-Assisted Discovery Learning Model on Improving Students' Mathematical Connections Hidayatsyah Hidayatsyah; Muhammad Fikry; Said Fadlan Anshari; Sudirman Sudirman
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

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Abstract

This study aims to analyze the effect of the Discovery Learning learning model assisted by Google Lens on improving students' mathematical connections. This study used a quasi-experimental method with a Non-equivalent Control Group Design involving high school/vocational high school students in Lhokseumawe City. The sample consisted of an experimental group using Google Lens and a control group using conventional learning. The results showed a significant increase in mathematical connection skills in the experimental group compared to the control group. Students' perceptions of the use of Google Lens were also positive, with indicators of increased learning motivation and engagement. These findings provide implications for the implementation of technology in mathematics learning.
Classification of Hospital Stay Duration for Schizophrenia Patients at RSUD Muyang Kute Using a Combination of C4.5 and Particle Swarm Optimization Putri Agustina Dewi; Munirul Ula; Said Fadlan Anshari
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25930

Abstract

Schizophrenia is a chronic mental disorder that often requires inpatient care, so an increase in the number of patients can lead to limited bed capacity in psychiatric wards. This study aims to classify the length of hospital stay for schizophrenia patients to support room requirement planning at RSUD Muyang Kute using the C4.5 algorithm optimized with Particle Swarm Optimization (PSO). The dataset consists of 657 medical records of inpatient schizophrenia cases from February 2023 to March 2025, categorized into three length-of-stay classes: short (1–5 days), medium (6–10 days), and long (>10 days). The C4.5 algorithm is used to construct a decision tree model based on historical data, while PSO is employed as an optimization method to improve the model configuration. The evaluation uses classification accuracy and Mean Absolute Percentage Error (MAPE) for room demand estimation. The results show that both the C4.5 and C4.5–PSO models achieve similarly high accuracy on the test data, while the manual MAPE calculation for room demand estimation yields a value of 52.66%. In contrast, the MAPE calculated by the system is 0.00% in the test scenario because all classes in the test data are correctly predicted. The web-based decision support system developed using Python and Streamlit is able to automatically provide predictions of length of stay and estimates of the required number of psychiatric beds at RSUD Muyang Kute.
Sistem Pendukung Keputusan Penentuan Golongan Ukt Bagi Calon Mahasiswa Baru Menggunakan Algoritma K-Nearest Neighbor Said Fadlan Anshari; Syahriani Putri Ayu; Fadlisyah Fadlisyah; Rizki Suwanda; Tri Ramdhany
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 1 (2026): Januari 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i1.271

Abstract

In continuing lectures, financial readiness is needed to finance education. Single Tuition Fee (Uang Kuliah Tunggal or UKT) is a tuition fee in one semester where there is only one type of fee collection based on the economic and social conditions of the student's parents/guardians so that each student's payment is not the same. The existence of these group differences plus the increase in the UKT group can trigger demonstrations at Malikussaleh University for new students of the class of 2023. Therefore, a decision support system is needed in grouping UKT groups. This study uses the K-Nearest Neighbor  algorithm with  a dataset of 1381 UKT data for new students class of 2023. Furthermore, a split dataset was carried out  by dividing 90% of training data and 10% of testing data. Then the attributes used consist of 13 attributes including father's income, mother's income, father's education, mother's education, father's job, mother's job, home status, house area, number of cars, number of motorcycles, number of brothers, number of working brothers, and number of younger siblings. The outputs produced in this study are classified into 7 classes, namely UKT 1, 2, 3, 4, 5, 6, and 7. The accuracy results obtained at K = 15 were 70.5% with an error value of  29.5% with the results of the number of data in UKT 1 as many as 16 people, UKT 2 as many as 38 people, UKT 3 as many as 27 people, UKT 4 as many as 32 people, UKT 5 as many as 26 people, and UKT 6, and UKT 7 as many as 0 people.
Implementation of ResNet-50 in a Fresh Fruit Bunch (FFB) Ripeness Detection System for Oil Palm M. Rafli Al Thoriq Mustafa; Muhammad Fikry; Said Fadlan Anshari
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6331

Abstract

The quality of Crude Palm Oil (CPO) is highly dependent on the accuracy of sorting the ripeness level of oil palm Fresh Fruit Bunches (FFB). Manual sorting processes currently used in factories are vulnerable to human error and subjectivity. This study aims to automate the objectivity of the sorting process using a deep learning model based on the ResNet-50 architecture with a transfer learning approach to classify FFB into three categories: Unripe, Ripe, and Overripe. The computational model was integrated into a web-based application using the Flask framework to support wireless operational use in factories. Experimental results showed a validation accuracy of 90.94% and an F1-score of 91%. Direct field validation using 42 primary data samples achieved a classification success rate of 83.33%. The implementation of a 75% confidence threshold proved effective in preventing prediction errors (zero misclassification), while the Cohen’s Kappa reliability test achieved a score of 0.769, indicating Substantial Agreement with expert evaluators. In conclusion, the ResNet-50-based system demonstrated reliable and objective performance and is considered ready for replication to maintain quality consistency in the palm oil processing industry.
Implementasi Metode Double Exponential Smoothing untuk Prediksi Jumlah Kebutuhan Air di PDAM Tirta Mon Pase Rahmatin Nisak; Arnawan Hasibuan; Said Fadlan Anshari; Rozzi Kesuma Dinata; Fadlisyah Fadlisyah
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5210

Abstract

Clean water is an essential human need, yet its provision is frequently disrupted by demand uncertainty, as experienced by PDAM Tirta Mon Pase with recurring public complaints regarding water supply interruptions. This study aims to design and implement a water demand forecasting system using the Double Exponential Smoothing (Holt’s Linear Trend) method and to evaluate its accuracy. The research utilized monthly historical water production data from January 2022 to December 2024 (36 observations) obtained from PDAM Tirta Mon Pase. The model was applied with smoothing parameters α = 0.8 and β = 0.2, and accuracy was measured using Mean Absolute Percentage Error (MAPE). The results show a very high level of accuracy with an overall MAPE of 3.56% (2022: 4.18%; 2023: 3.91%; 2024: 2.65%), and the forecast predicts water demand in December 2027 will reach 1,131,071.39 m³. It can be concluded that the Double Exponential Smoothing method is highly accurate and effective for forecasting water demand at PDAM Tirta Mon Pase. The developed system is therefore strongly recommended for operational adoption as a strategic decision-support tool in water resource planning, production, and infrastructure development.
Penerapan Metode Naïve Bayes Dalam Sistem Rekomendasi Pemilihan Program Studi Pendidikan Tinggi Berbasis Website Rizki Suwanda; Said Fadlan Anshari; Rizky Putra Fhonna; Tulus Setiawan
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15858

Abstract

Pemilihan program studi pendidikan tinggi merupakan keputusan penting yang berdampak pada arah karir dan pengembangan potensi siswa. Namun, banyak siswa mengalami kesulitan dalam menentukan pilihan yang sesuai dengan minat dan kemampuan akademik mereka. Dalam praktiknya, pemilihan program studi masih sering dilakukan secara subjektif tanpa dukungan data atau sistem yang dapat membantu proses pengambilan keputusan secara rasional dan terukur. Penelitian ini bertujuan untuk mengembangkan sebuah sistem rekomendasi berbasis metode Naïve Bayes yang mampu memberikan saran program studi kepada siswa berdasarkan data minat dan prestasi akademik. Metode Naïve Bayes dipilih karena mampu mengklasifikasikan data secara efisien dengan pendekatan probabilistik, meskipun asumsi antar atribut bersifat independen. Sistem ini diharapkan dapat menjadi alat bantu bagi siswa maupun pihak sekolah (seperti guru BK) dalam memberikan arahan akademik berbasis data. Tahapan penelitian dimulai dari studi literatur dan perancangan sistem, dilanjutkan dengan pengumpulan data berupa minat siswa dan nilai akademik, baik melalui dataset simulasi maupun data uji terbatas dari responden nyata. Data tersebut kemudian diproses dan digunakan untuk membangun model klasifikasi menggunakan algoritma Naïve Bayes. Selanjutnya, sistem diuji untuk mengukur akurasi dan efektivitasnya dalam memberikan rekomendasi program studi yang sesuai. Penelitian ini juga mencakup evaluasi sistem berdasarkan hasil klasifikasi serta analisis keterkaitan antara input (minat dan prestasi) dan output rekomendasi program studi.
Co-Authors Al-Ghiyats, Said Ananda, Ria Angkasa, M. Ishlah Buana Arnawan Hasibuan Bustami Bustami Chan, Fajar Ricaldi Dahlan Abdullah Dani Ramdani Dinda, Dinda Fadlisyah Fadlisyah Fahrezi, Irgi Fanita, Fanita Hidayatsyah Hidayatsyah Husna, Nur Fazri Juliawati Khairi, Abil Lidya Rosnita Limbong, Hendra Putranta M Ishlah Buana Angkasa M. Rafli Al Thoriq Mustafa Maulana, O.K. Muhammad Majid Mhd Firza Ryzaaldy Muhammad Daud Muhammad Fikry Mukti Qamal Mukti Qamal Munirul Ula Pohan, Fadlyani Putri Agustina Dewi Putri, Nazirah Allisya Rahmatin Nisak Rini Meiyanti Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal, Reyhan Achmad Rizki Suwanda Rizki, Agus Maula Rizki, Sukma Rizky Putra Fhonna Rozzi Kesuma Dinata Sahputra, Irfan Samudera, Brucel Duta Sandy, Cut Lika Mestika Sartika, Tiara Sudirman Sudirman Sujacka Retno Sukiman, T. Sukma Achriadi Sukma Rizki Syahriani Putri Ayu Syibral Malasyi, Syibral Tri Ramdhany Tulus Setiawan Wardina Ningsih Yasin, Fijri Ahmad Zalfie Ardian Zara Yunizar Zarayunizar Zarayunizar