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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Resona : Jurnal Ilmiah Pengabdian Masyarakat Jurnal SOLMA Jurnal Teknoinfo MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Tekno Kompak Jurnal Informatika Kaputama (JIK) Jurnal Ners G-Tech : Jurnal Teknologi Terapan Rambideun : Jurnal Pengabdian Kepada Masyarakat Jurnal Tika Brilliance: Research of Artificial Intelligence Sisfo: Jurnal Ilmiah Sistem Informasi Electronic Journal of Education, Social Economics and Technology Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Hafizh Al Kautsar Aidilof
Program Studi Teknik Informatika, Universitas Malikussaleh, Jalan Cot Tgk Nie-Reulet, Aceh Utara, Indonesia, 243552
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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IMPLEMENTASI PEMODELAN CITRA MODEL SVM (SUPPORT VECTOR MACHINE) DALAM PENENTUAN PENGKLASIFIKASIAN JENIS SUARA KONTES BURUNG Rosdiana Rosdiana; Mutammimul Ula; Hafizh Al Kautsar Aidilof
Jurnal Informatika Kaputama (JIK) Vol 5 No 2 (2021): Volume 5, Nomor 2, Juli 2021
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v5i2.264

Abstract

Penelitian ini digunakan untuk klasifikasi pemodelan dengan citra model SVM (Support Vector Machine) dalam pengklasifikasian jenis suara burung yang telah dilatih dan hasil yang didapat berupa jenis suara burung yang telah diklasifikan dengan uji latih model SVM dihasilkan. Selanjutnya proses pada pengenalan suara burung yang dilakukan secara proses otomatis penggalian dan penentuan informasi linguistik yang disampaikan oleh sinyal suara atau sirkuit elektronik. Untuk masing-masing data latih memiliki tiap-tiap sample suara yang dihasilkan memiliki nilai energi masing-masing yang dipengaruhi oleh frekuensi, amplitudodan phasa. Nilai energi dari masing-masing sample suara itu kemudian ditetapkan sebagai suatu ciri untuk dapat dikalsifikasi dengan sample suara lainnya. Metode support vector machine berperan dalam proses pengelompokan nilai energi suatu untuk menentukan ciri dari suatu sample suara. Setelah masing-masing sample suara memiliki identitas atau ciri masing-masing,maka dilakukanlah pengklasifikasian sample suara dimana dalam penelitian ini akan ditampilkan spesies dari suara burung yang diinputkan. dalam skema identifikasi jenis burung memiliku proses dengan tahap kenel 1 proses SVM dari masing-masing input file suara dan dilakukan kekernel uji dengan proses SVM yang hasilnya fungsi mapping, hasil uji, jarak cektor ciri spesies burung dan nilai grafik scope yang di latih. Persentase keakuratan sistem grafik dengan identifikasi false dan right berdasrkan sample pelatihan yang dilakukan.
Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19 Nurdin Nurdin; Erni Susanti; Hafizh Al-Kautsar Aidilof; Dadang Priyanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2280

Abstract

COVID-19 is a respiratory infection disease caused by the corona virus. Transmission of this virus can spread very quickly so that the number of cases of the corona virus continues to grow and becomes an epidemic that spreads not only in Indonesia but also in other countries in the world. The purpose of this study is to build an expert system that is able to diagnose Covid-19 early by using a comparison of the Nave Bayes method and the Dempster Shafer method. The amount of data used in this study is 550 data, consisting of 500 training data and 50 testing data. While the variables used are symptoms related to COVID-19 as many as 17 symptoms consisting of G01, G02, G03, G04, G05, G06, G07, G08, G09, G10, G11, G12, G13, G14, G15, G16, G17. The diagnostic data consists of Suspected (PDP), Non-Suspected, and Close Contact (ODP). The results of the percentage test by comparing system diagnoses with expert diagnoses, for the nave Bayes method it has an accuracy of 96% with 48 diagnoses according to expert diagnoses from 50 tested data. Meanwhile, the Dempster Shafer method has an accuracy of 40% with 20 diagnoses according to expert diagnoses from 50 tested data. Based on the results of this study, the Naive Bayes and Dempster Shafer methods can be applied to an expert system for early diagnosis of COVID-19, from the results of the system testing the Naive Bayes method has better accuracy than the Dempster Shafer method.
Sistem Informasi Geografis ( Sig ) Pemetaan Daerah Rawan Bencana Banjir di Kec. Lhoksukon Kab. Aceh Utara Berbasis Web Menggunakan Metode K-Medoids Karimullah Karimullah; Bustami Bustami; Hafizh Al Kautsar
Jurnal Ners Vol. 9 No. 3 (2025): JULI 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v9i3.46244

Abstract

Abstrak Banjir merupakan bencana alam yang sering terjadi di Indonesia, termasuk di Kecamatan Lhoksukon, Kabupaten Aceh Utara. Minimnya informasi spasial mengenai daerah rawan banjir menghambat upaya mitigasi dan penanggulangan bencana secara efektif. Penelitian ini bertujuan untuk membangun sebuah Sistem Informasi Geografis (SIG) berbasis web guna memetakan daerah rawan bencana banjir dengan menerapkan metode clustering K-Medoids. Data yang digunakan berasal dari Badan Penanggulangan Bencana Daerah (BPBD) Aceh Utara tahun 2017–2021 serta hasil wawancara dengan masyarakat setempat. Sistem ini mampu mengelompokkan daerah berdasarkan tingkat kerawanan banjir berdasarkan beberapa parameter seperti struktur tanah, kemiringan tanah, dan penggunaan lahan. Metode K-Medoids dipilih karena memiliki ketahanan yang lebih baik terhadap outlier dibanding metode lain seperti K-Means. Hasil sistem divisualisasikan dalam bentuk peta digital yang dapat diakses melalui web untuk memudahkan pihak BPBD dan masyarakat dalam memahami distribusi kerawanan banjir. Dengan adanya sistem ini, diharapkan dapat membantu dalam pengambilan keputusan yang lebih cepat dan akurat dalam upaya penanggulangan bencana banjir di masa depan. Kata Kunci: Sistem Informasi Geografis, Banjir, K-Medoids, Clustering, Aceh Utara Abstract Flooding is a frequent natural disaster in Indonesia, including in Lhoksukon Subdistrict, North Aceh Regency. The lack of spatial information regarding flood-prone areas hampers effective disaster mitigation and response. This research aims to develop a web-based Geographic Information System (GIS) to map flood-prone areas using the K-Medoids clustering method. The data used were obtained from the North Aceh Regional Disaster Management Agency (BPBD) for the years 2017–2021 and interviews with local residents. The system classifies regions based on flood vulnerability levels using parameters such as soil structure, land slope, and land use. The K-Medoids method was chosen for its superior resilience to outliers compared to other methods such as K-Means. The results are visualized through a web-accessible digital map to help BPBD and the public understand the spatial distribution of flood risk. This system is expected to support quicker and more accurate decision-making for future flood disaster management. Keywords: Geographic Information System, Flood, K-Medoids, Clustering, North Aceh
Implementation of the Double Exponential Smoothing Method in Predicting Palm Oil Harvest Yields Muhammad Raihan Rangkuti; Taufiq; Hafizh Al Kautsar Aidilof
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Double Exponential Smoothing (DES) is a forecasting method that combines two main components: level and trend. This method is used for data that shows a trend pattern, meaning data that tends to increase or decrease over time. This study aims to implement the Double Exponential Smoothing method to predict oil palm yields at PT. Amal Tani. The data used in this study consists of historical oil palm yield data from 2019 to 2023. The prediction system designed is web-based, utilizing PHP programming language and MySQL database. The performance evaluation of the prediction model is conducted using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) metrics. The study demonstrates that the Double Exponential Smoothing method can produce accurate and effective predictions. The implementation of this system facilitates data processing and the dissemination of information related to oil palm yields. The results indicate that this prediction model can assist the management of PT. Amal Tani in making more accurate yield forecasts, thereby increasing productivity and operational efficiency. The implementation of this method is also expected to ease the company’s decision-making process regarding production planning and seed planting. This study concludes that the Double Exponential Smoothing method is an effective and accurate tool for predicting oil palm yields and provides positive contributions to data management and decision-making processes at PT. Amal Tani. This study offers insights into the application of the Double Exponential Smoothing method in forecasting oil palm yields.
Influential Factors of Activity Patterns and Distribution Patterns of Street Vendors in the GOR Haji Agus Salim Stadium Area on Public Space in Padang City, West Sumatra M Yusuf Qardawi; Rinaldi Mirsa; Soraya Masthura Hassan; Effan Fahrizal; Hafizh Al Kautsar Aidilof
Electronic Journal of Education, Social Economics and Technology Vol 6, No 2 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i2.988

Abstract

This research discusses how the activities and distribution patterns of street vendors (PKL) influence public space in the GOR Haji Agus Salim Stadium area, Padang City, West Sumatra. The increasing number of street vendors in this area has raised spatial planning issues, limited accessibility, and disrupted the original function of the stadium as a sports facility. This study uses a qualitative descriptive approach, with data collected through field observations, interviews, and documentation. The results show that PKL activities are strongly influenced by their proximity to formal sector activities and pedestrian flow. Their distribution patterns are classified into two types: clustered (focus agglomeration) and linear, following road networks (linear agglomeration). Factors influencing these patterns include strategic location, accessibility, types of goods sold, and the facilities used for vending. The study reveals that the presence of street vendors significantly affects the quality of public space visually, functionally, and in terms of comfort. Therefore, a comprehensive management strategy is needed that balances the economic needs of PKL with the order and proper use of urban space.
Interactive Visualization of Food Security Trends in North Aceh with a Business Intelligence Dashboard Lidya Rosnita; Muhammad Ikhwani; Hafizh Al Kautsar Aidilof; Muhammad Muaz Munauwar
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7190

Abstract

Food security in North Aceh Regency faces multifaceted challenges, including production fluctuations, price instability, and fragmented monitoring data across various institutions. These issues often hinder timely decision-making and the formulation of effective policies. Therefore, this study aims to develop a comprehensive Business Intelligence (BI) dashboard that can interactively visualize food security trends in North Aceh to support data-driven and evidence-based decision-making. The research methodology involves integrating data from multiple sources such as the Central Bureau of Statistics (BPS) and the Department of Agriculture using the ETL (Extract, Transform, Load) process to ensure consistency and accuracy. A data warehouse was then designed to store and manage the consolidated datasets efficiently, followed by the development of an interactive visual dashboard as the main analytical tool. The resulting dashboard is capable of visualizing six key parameters of food security through thematic maps, trend graphs, and comparative charts that allow users to observe temporal and spatial patterns. Advanced interactive features such as filtering, drill-down analysis, and cross-filtering provide users with the flexibility to independently explore data from different perspectives. The analysis demonstrates that the BI dashboard effectively integrates fragmented datasets, simplifies complex information, and enhances analytical capabilities for stakeholders. Overall, the findings indicate that implementing an interactive BI dashboard is a strategic and innovative solution to transform food security monitoring in North Aceh into a more proactive, integrated, and adaptive governance system, thereby strengthening regional resilience and policy responsiveness.
Automated Testing of Surah Yasin Memorization Using Discrete Cosine Transform (DCT) for Voice Recognition Rizky Darma Putra; Fadlisyah Fadlisyah; Hafizh Al Kautsar Aidilof
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26789

Abstract

Memorizing the Qur'an is a highly recommended act of worship for Muslims, yet traditional learning methods often face constraints such as the limited availability of competent teachers. This research aims to develop and measure the performance of an automated system for testing the memorization of Surah Yasin (verses 1-83) through voice recognition using the Discrete Cosine Transform (DCT) method. The DCT method is applied to convert voice signals from the time domain to the frequency domain, extracting unique features from each verse to be compared with reference voice samples. The system was tested on all 83 verses using 6 training voice samples and 4 test voice samples per verse, totaling 830 samples. System performance was evaluated using four variations of probability constants (error tolerance): 0.3, 0.4, 0.5, and 0.6. The results indicate that the probability constant significantly affects the system's accuracy. The detection rates achieved were 70.2% (constant 0.3), 84.6% (constant 0.4), 89.8% (constant 0.5), and peaked at 93.4% (constant 0.6). With an overall average detection rate of 84.5%, the DCT method is proven effective, and the developed system has strong potential as a reliable aid for Qur'an memorizers.
Co-Authors Ali Imran Nasution Ananda Faridhatul Ulva Angga Pratama Ar Razi Ar Razi Asrillah Asrillah Bustami Bustami Dadang Priyanto Dahlan Abdullah Desuriyan Ramadhan Desvina Yulisda Effan Fahrizal Erni Susanti Fadlisyah Fadlisyah Habib Muharry Yusdartono Ilham Saputra Karimullah Karimullah Khairat Nur Lidya Rosnita M Yusuf Qardawi Muhammad Habib Muhammad Ikhwani Muhammad Muaz Munauwar Muhammad Muhammad Muhammad Raihan Rangkuti Muhammad Zikri Mutammimul Ula Muzakir Nur Nurdin Nurdin Rahma Fitria Rinaldi Mirsa Rizal S.Si., M.IT, Rizal Rizky Darma Putra Rizky Putra Fhonna Rosdiana Rosdiana Soraya Masthura Hassan Taufiq Taufiq Wiwik Handayani Yoga Pangestu