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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 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
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 Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Nursing Public Health Social Sciences 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.
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 Erni Susanti Habib Muharry Yusdartono Ilham Saputra Karimullah Karimullah Khairat Nur Muhammad Habib Muhammad Muhammad Muhammad Raihan Rangkuti Muhammad Zikri Mutammimul Ula Muzakir Nur Nurdin Nurdin Rahma Fitria Rizal S.Si., M.IT, Rizal Rizky Putra Fhonna Rosdiana Rosdiana Taufiq Taufiq Wiwik Handayani Yoga Pangestu