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All Journal Teknika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal SOLMA JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization International Journal of Engineering, Science and Information Technology JURNAL ILMIAH GLOBAL EDUCATION TECHSI - Jurnal Teknik Informatika Jurnal Malikussaleh Mengabdi Jurnal Pengabdian Nasional (JPN) Indonesia Journal of Advanced Computer Knowledge and Algorithms
Aidilof, Hafizh Al Kautsar
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Fundamental Analysis in Choosing Altcoins in Cryptocurrency With Preference Selection Index Method Ritonga, Huan Margana; Yunizar, Zara; Aidilof, Hafizh Al Kautsar
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.848

Abstract

Cryptocurrency has become one of the most intriguing topics in finance and technology in recent years. With the growing prominence of Bitcoin, the rise of altcoins (alternative cryptocurrencies) also demonstrates significant potential within the cryptocurrency market. Altcoins, which include all cryptocurrencies other than Bitcoin, offer diverse functionalities and use cases, ranging from smart contracts to decentralized finance (DeFi) applications. This thesis identifies the altcoin options with the best investment opportunities and the highest growth potential. The study employs the Preference Selection Index (PSI) method, a multi-criteria decision-making approach that evaluates alternatives based on specific preferences and criteria. This method is particularly suitable for assessing complex investment decisions involving multiple variables, such as market capitalization, technological innovation, and utility. By applying PSI, investors can decide which altcoins will likely yield substantial returns. A web-based platform has been developed as part of this research to simplify selecting promising altcoins. This platform enables users to evaluate options based on predefined criteria, such as market trends, project objectives, and development team credibility. The accessibility of this tool empowers users—whether novice or experienced investors—to navigate the dynamic cryptocurrency market more effectively. Altcoins provide a unique opportunity for diversification in investment portfolios. Unlike Bitcoin, which is often viewed as a store of value, many altcoins are designed with specific purposes and innovative features. For instance, Ethereum introduced smart contracts that revolutionized decentralized applications, while other altcoins focus on scalability or niche markets like the Internet of Things (IoT). However, investing in altcoins also comes with challenges like high market volatility, security risks, and regulatory uncertainties. Therefore, thorough research and strategic planning are essential for minimizing risks while maximizing returns in this rapidly evolving sector.
Student Learning Style Decision-Making System Using the Multi-Attribute Utility Theory Method at SMA Negeri 1 Jangka Munawarah, Munawarah; Fuadi, Wahyu; Aidilof, Hafizh Al Kautsar
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.842

Abstract

Education plays a vital role in shaping individual development and national progress. One key factor influencing learning effectiveness is students' learning styles, which determine how individuals absorb, organize, and process information. Understanding these differences is crucial for designing effective teaching methods. This research develops a Decision Support System (DSS) to determine student learning styles at SMA Negeri 1 Jangka using the Multi-Attribute Utility Theory (MAUT) method. MAUT is chosen for its ability to evaluate multiple criteria, convert them into numerical values, and systematically identify the most suitable learning approach. The alternatives in this study include Project Based Learning (PBL), Problem-Based Learning (PrBL), Inquiry-Based Learning (IBL), Discovery Learning (DL), and Contextual Teaching and Learning (CTL). The MAUT analysis considers five criteria: student activeness, material understanding, collaboration, initiative and creativity, and teacher-student communication. The research stages include literature study, data collection, system and database design, MAUT implementation, and system evaluation. The results, based on MAUT calculations, show that Inquiry-Based Learning (IBL) scores the highest at 13.611, followed by Discovery Learning (DL) at 13.018, Problem-Based Learning (PrBL) at 12.975, Contextual Teaching and Learning (CTL) at 12.929, and Project Based Learning (PBL) at 12.558. This system assists educators in designing personalized learning strategies that align with students' strengths. Leveraging data-driven analysis enhances education quality, fosters a student-centred learning environment, and improves academic performance and lifelong learning habits.
Clustering of Data Monitoring Water Quality Using Mean-Shift Clustering Method Aidilof, Hafizh Al Kautsar; Rosnita, Lidya; Kurniawati, Kurniawati; Ikhwani, Muhammad
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 1 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i1.22390

Abstract

This study aims to cluster water quality data from Nile tilapia ponds using the Mean Shift Clustering method. The parameters used to analyze water quality include temperature, pH, turbidity, and salinity, which are crucial factors for the growth and health of Nile tilapia. The data used in this research consist of water quality measurements from several Nile tilapia ponds. The clustering process seeks to identify groups of data with similar water quality characteristics, providing insights into optimal environmental conditions for tilapia farming. The clustering results reveal several distinct groups of water quality based on variations in temperature, pH, turbidity, and salinity. Results of the experiment show that a bandwidth value of 400 successfully identifies a relatively simple number of clusters, specifically four clusters. The Mean Shift Clustering method proves effective in grouping data without requiring assumptions about data distribution and can detect clusters with arbitrary shapes. Consequently, the findings of this study can be used to provide recommendations for improving water quality to enhance tilapia pond productivity.
Implementasi Sistem Informasi Geografis untuk Pelacakan IP Address Daro Domain Menjadi Peta Interaktif Fachruzi, Faza Reihan; Adek, Rizal Tjut; Aidilof, Hafizh Al Kautsar
Jurnal Ilmiah Global Education Vol. 6 No. 2 (2025): JURNAL ILMIAH GLOBAL EDUCATION, Volume 6 Nomor 2
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/jige.v6i2.3813

Abstract

This research aims to develop a geographic information system (GIS) capable of tracking the IP address of a domain and visualizing it in the form of an interactive map. In the context of computer networks, IP geolocation is an important aspect in detecting user location for various purposes such as service personalization, network performance improvement through Content Delivery Network (CDN), and compliance with certain regional laws. The system built utilizes the Sequential Search algorithm to facilitate the search for data such as food prices from various markets. The tracking process starts from converting the domain into an IP address, followed by a traceroute to determine the path (hops) through which the data packet travels, and finally mapping the results visually using GIS. The results show that the system is able to identify public, private, and inactive IPs, as well as display data communication routes with marked points on the map. This visualization helps users in analyzing the network, detecting potential disruptions, and making it easier to understand the flow of data traffic. The system is also relevant for use in network monitoring, suspicious activity tracking, and education about internet infrastructure.
Implementasi Algoritma XGBoost dengan Walk Forward Validation untuk Prediksi Harga Emas Antam Hisyam, Mochammad; Fitri, Zahratul; Aidilof, Hafizh Al Kautsar
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8693

Abstract

Accurate gold price prediction is crucial in supporting financial and investment decision-making. This study aims to develop and optimize a daily gold price prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on historical price data and technical indicators. The model was constructed to predict two types of prices, namely "Close" and "Buyback" prices in IDR/gram. Optimization was carried out using Bayesian Optimization to obtain the best hyperparameter combinations. The model was evaluated using a Walk Forward Validation (WFV) approach with a 14-day sliding window and two main evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that the model provides excellent predictive performance, with an average RMSE of 15,431.92 and MAPE of 1.03% for Close price, and RMSE of 15,382.64 and MAPE of 1.15% for Buyback price. The prediction visualizations indicate that the model consistently follows the actual price trend. Feature importance analysis reveals that technical indicators such as RSI, EMA, and MACD significantly contribute to the model. The success of this study demonstrates that an optimized XGBoost model can serve as a reliable approach for gold price forecasting and opens opportunities for developing more advanced predictive models in future research.
Pembudidayaan Bonsai untuk Mengurangi Kemiskinan di Desa Uteunkot Lhokseumawe A, Hendra; Saputra, Eri; Aidilof, Hafizh Al Kautsar; Qardawi, Muhammad Yusuf; Al-Shahzam , Mohd. Hafez
Jurnal Malikussaleh Mengabdi Vol. 4 No. 2 (2025): Jurnal Malikussaleh Mengabdi, Oktober 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i02.24760

Abstract

Desa Uteunkot di Kecamatan Muara Dua memiliki potensi sumber daya alam berupa tanaman yang dapat dikembangkan menjadi bonsai. Namun, potensi ini belum termanfaatkan secara optimal untuk meningkatkan kesejahteraan masyarakat. Tingginya angka pengangguran di kalangan usia produktif menjadi salah satu permasalahan utama yang perlu dicarikan solusi. Program pengabdian ini bertujuan untuk mentransformasikan seni bonsai dari sekadar hobi menjadi sebuah unit usaha produktif yang dapat menjadi sumber penghasilan alternatif bagi masyarakat. Melalui pelatihan intensif, masyarakat tidak hanya akan dibekali keterampilan teknis membentuk bonsai yang artistik, tetapi juga pengetahuan manajemen usaha, branding, dan pemasaran digital. Program ini diharapkan dapat memberdayakan masyarakat secara ekonomi, mengurangi angka kemiskinan, dan menjadikan Desa Uteunkot sebagai sentra bonsai yang dikenal di tingkat lokal maupun regional, yang pada akhirnya menciptakan siklus ekonomi berkelanjutan berbasis potensi lokal.
Implementasi Algoritma XGBoost dengan Walk Forward Validation untuk Prediksi Harga Emas Antam Hisyam, Mochammad; Fitri, Zahratul; Aidilof, Hafizh Al Kautsar
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8693

Abstract

Accurate gold price prediction is crucial in supporting financial and investment decision-making. This study aims to develop and optimize a daily gold price prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on historical price data and technical indicators. The model was constructed to predict two types of prices, namely "Close" and "Buyback" prices in IDR/gram. Optimization was carried out using Bayesian Optimization to obtain the best hyperparameter combinations. The model was evaluated using a Walk Forward Validation (WFV) approach with a 14-day sliding window and two main evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that the model provides excellent predictive performance, with an average RMSE of 15,431.92 and MAPE of 1.03% for Close price, and RMSE of 15,382.64 and MAPE of 1.15% for Buyback price. The prediction visualizations indicate that the model consistently follows the actual price trend. Feature importance analysis reveals that technical indicators such as RSI, EMA, and MACD significantly contribute to the model. The success of this study demonstrates that an optimized XGBoost model can serve as a reliable approach for gold price forecasting and opens opportunities for developing more advanced predictive models in future research.
Heart Disease Classification Based on Medical Record Data Using the Logistic Regression Method Iswari, Syahyana; Dinata, Rozzi Kesuma; Aidilof, Hafizh Al Kautsar
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.8867

Abstract

Heart disease remains one of the primary causes of mortality globally and poses a significant public health concern, including in Indonesia. Early identification of individuals at risk is essential for lowering death rates and enhancing the success of medical interventions. This research focuses on developing a classification model for heart disease using the Logistic Regression technique, utilizing data extracted from patient medical records. The dataset comprises 100 entries, each containing six key features: age, gender, blood pressure, heart rate, respiratory rate, and chest pain. The model was trained on 80% of the data and evaluated using the remaining 20%. Model performance was assessed using several metrics, including accuracy, precision, recall (sensitivity), F1-score, confusion matrix, and the ROC (Receiver Operating Characteristic) curve. The evaluation results revealed an accuracy of 95%, precision of 100%, recall of 88.89%, F1-score of 94.12%, and an AUC score of 0.99. These outcomes suggest that Logistic Regression is highly effective for classifying heart disease risk and can serve as a valuable tool in early detection systems supported by medical record data.
Pendampingan Implementasi dan Pelaksanaan E-office bagi Aparatur Gampong Cot Keumuneng Kabupaten Aceh Utara untuk Mendukung Smart governance Aidilof, Hafizh Al Kautsar; Rosnita, Lidya; Fitria, Rahma; Meiyanti, Rini; Yusdartono, Habib Muharry; Rangkuti, Haris Yunanda; Azwir, Andrea Micola
Jurnal SOLMA Vol. 15 No. 1 (2026)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v15i1.16410

Abstract

Background: Perkembangan teknologi khususnya tekologi informasi telah merubah pola pelayanan masyarakat desa sebagai unit terkecil dari pemerintahan Republik Indonesia, dari yang sebelumnya terbatas pada ruang dan waktu, kini telah fleksibel dan lebih leluasa. Pengabdian kepada masyarakat ini dilakukan untuk mendukung program pemerintah menerapkan konsep smart village dimana dalam pelaksanaan administrasinya menerapkan smart government. Metode: Kegiatan pengabdian kepada masyarakat ini menyasar Aparatur Desa Cot Keumuneng yang berjumlah 15 orang sebagai peserta pelatihan yang nantinya akan menggunakan aplikasi e-office. Setelah pelatihan dan pendampingan dilakukan survey untuk melihat pemahaman aparatur desa dalam menggunakan e-office. Hasil: Kegiatan pengabdian kepada masyarakat ini mendapat hasil positif di kalangan aparatur desa dimana dengan adanya aplikasi ini proses persuratan di kalangan aparatur desa menjadi lebih efektif dan efisien. Dampak lainnya adalah pelayanan kepada masyarakat dapat lebih ditingkatkan karena fleksibilitas yang didapat dengan penerapan teknologi informasi. Kesimpulan: Kegiatan pengabdian kepada masyarakat ini sangat bermanfaat baik bagi internal aparatur desa maupun bagi pelayanan kepada warga desa sendiri dan aparatur desa berharap kegiatan serupa dapat terus dilaksanakan secara berkesinambungan.
Klasifikasi Penyakit Jantung Berbasis Data Rekam Medis Menggunakan Algoritma Local Mean K-Nearest Neighbor Putri, Alayda Zaielamy; Daud, Muhammad; Aidilof, Hafizh Al Kautsar
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.134-143

Abstract

Penyakit jantung merupakan penyebab kematian utama di dunia dengan diagnosis dini yang penting namun sering terkendala akurasi interpretasi data rekam medis kompleks. Algoritma klasifikasi tradisional seperti K-NN memiliki kelemahan dalam menangani noise dan outliers dalam data medis. Penelitian ini bertujuan mengimplementasikan algoritma Local Mean K-Nearest Neighbor untuk mengklasifikasikan penyakit jantung berdasarkan data rekam medis dengan akurasi yang lebih baik. Dataset terdiri dari 403 observasi dengan 10 variabel meliputi jenis kelamin, umur, tekanan darah, heart rate, respiratory rate, hasil elektrokardiogram, kondisi nyeri dada, dan klasifikasi diagnosis. Metode Local Mean K-NN mengadaptasi konsep K-NN tradisional dengan pendekatan local mean calculation untuk mengatasi noise dan outliers. Tahapan penelitian mencakup preprocessing data, feature encoding, feature scaling, hyperparameter tuning, dan evaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil menunjukkan algoritma Local Mean K-NN dengan nilai K optimal 11 mampu mengklasifikasikan penyakit jantung dengan accuracy 71.60%, precision 69.21%, recall 71.60%, dan F1-score 70.27%. Model menunjukkan performa sangat baik dalam mendeteksi Penyakit Jantung Koroner dengan precision 91.89% dan recall 97.14%. Analisis feature importance mengidentifikasi nyeri dada sebagai indikator terpenting (73.79%), diikuti heart rate (36.40%) dan respiratory rate (25.25%). Penelitian membuktikan efektivitas Local Mean K-NN sebagai clinical decision support tool dalam klasifikasi penyakit kardiovaskular meskipun terdapat tantangan class imbalance pada kelas minoritas.
Co-Authors A, Hendra Adi Prasetyo Al-Shahzam , Mohd. Hafez Angelina, Difa Asrianda Asrianda Azwir, Andrea Micola Eri Saputra Fachruzi, Faza Reihan Fakhrurrazi Fuadi, Wahyu Habib Muharry Yusdartono Hamsi, Widia Hisyam, Mochammad Iswari, Syahyana Kurniawati Kurniawati Lidya Rosnita Lubis, Aulia Azzahra Ma'aruf Marpaung, Rifky Firzani Muhammad Daud Muhammad Ikhwani Muhammad Muaz Munauwar Munawarah Munawarah, Munawarah Na'syakban, Irvan Nurdin Nurdin Putri, Alayda Zaielamy Qardawi, Muhammad Yusuf Rahma Fitria, Rahma Rangkuti, Haris Yunanda Rini Meiyanti Ritonga, Huan Margana Rizal S.Si., M.IT, Rizal Rozzi Kesuma Dinata Ruzanna, Arina Sahputra, Ilham salamah salamah Samudera, Brucel Duta Veri Ilhadi Zahratul Fitri, Zahratul Zara Yunizar Zohra, Siti Fatimah A Zulfadli Zulfadli