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Aidilof, Hafizh Al Kautsar
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Pelatihan dan Pendampingan Teknologi Informasi Pengembangan Gampong Digital Gampong Uteunkot Berbasis Web di Kota Lhokseumawe Ilhadi, Veri; Aidilof, Hafizh Al Kautsar; Fakhrurrazi; Sahputra, Ilham; Zohra, Siti Fatimah A; Angelina, Difa
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpni.v5i3.1064

Abstract

This program aims to enhance information technology capabilities in Uteunkot Village, Lhokseumawe City, focusing on developing web-based digital villages. The initiative includes training and assistance for village residents to support the village apparatus in public services, archiving, and marketing for MSMEs. The training aims to facilitate archiving at the Geuchik office through digital public service and archiving socialization, accompanied by website development training for the village. The web application is designed to present relevant and beneficial information for village residents with an efficient interface. The results of the digital web training and assistance indicate that villages in Indonesia are now more connected and can access broader information, contributing to increased community knowledge. The digitalization of public services has accelerated administrative processes, enhanced transparency, and facilitated interactions between village governments and their residents. Additionally, the training enhances the digital skills of village officials, increasing their capacity to utilize web-based technology. The implications of this training suggest that villages can transform to be smarter and more competitive in the digital era
Classification of Heart Disease Using Modified K-Nearest Neighbor (MKNN) Method Lubis, Aulia Azzahra Ma'aruf; Dinata, Rozzi Kesuma; Aidilof, Hafizh Al Kautsar
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 2 (2024): Journal of Advanced Computer Knowledge and Algorithms - April 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i2.15702

Abstract

Penyakit jantung memiliki banyak jenis dan gejala yang dialami. Penyakit jantung adalah sebuah kondisi ketika organ jantung tidak dapat bekerja sebagaimana fungsinya dengan baik. Jantung adalah organ penting dalam tubuh manusia yang dimana fungsinya adalah memompa darah ke seluruh tubuh. Karena itu dibutuhkannya diagnosa awal untuk pencegahan penyakit jantung dengan memanfaatkan system yang dapat dibuat untuk diagnosa awal pada gejala yang dialami. Yang pada penelitian ini akan menggunakan metode Modified K-Nearest Neighbor (MKNN) dalam mengklasifikasikan penyakit jantung berdasarkan kriteria atau gejala yang ada. Penelitian ini menggunakan 6 kriteria penyakit dan 3 kelas diagnosa penyakit jantung. Dengan melewati beberapa langkah pengerjaan yaitu menghitung jarak Euclidean, menghitung nilai validitas dan terakhir menghitung weight voting dengan mengandalkan nilai K yang telah ditentukan sejak awal perhitungan. Pada penelitian ini telah ditentukan nilai K=5 dan didapat hasil pengujian akurasi sebesar 85%, dengan recall 90% dan precision 85%.
Implementasi Algoritma K-Medoid pada Clustering Sayuran Unggulan di Kabupaten Aceh Utara Meiyanti, Rini; Munauwar, Muhammad Muaz; Fitria, Rahma; Aidilof, Hafizh Al Kautsar
TEKNIKA Vol. 19 No. 1 (2025): Teknika Januari 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14378546

Abstract

Klasterisasi tanaman pada kelompok tani adalah proses pengelompokan tanaman berdasarkan karakteristik tertentu, seperti jenis tanaman, musim tanam, atau lokasi geografis, dengan tujuan meningkatkan efisiensi produksi. Kelompok Tani KWT Meugah Raya masih belum mampu melebihi hasil produksi pertanian sayuran di Aceh Utara. Metode data mining dapat mengidentifikasi pola-pola menarik dalam kumpulan data, salah satunya adalah algoritma K-Medoids clustering yang mengelompokkan data berdasarkan kesamaan karakteristik. Cluster terbentuk dengan menghitung sejauh mana kedekatan antara medoid dan objek non-medoids. Data yang digunakan adalah data dari Badan Pusat Statistik (BPS) pada tahun 2021-2023 di Kabupaten Aceh Utara mengumpulkan data dari 5 kategori sayuran dan 4 variabel, meliputi luas panen, produksi, luas tanaman, dan luas penanaman baru. Melalui algoritma K-Medoids, hasil klastering sayuran unggulan menghasilkan pengelompokan potensi ke dalam 3 klaster, yaitu klaster rendah (C1), sedang (C2), dan tinggi (C3) dengan mengumpulkan macam-macam data sayuran yang ditanam oleh masyarakat setempat berupa cabai besar, kacang panjang, kangkung, terong dan tomat. Langkah berikutnya adalah Menentukan nilai titik pusat awal dengan menyusun data berdasarkan urutan dari yang terendah hingga tertinggi pada setiap data variabel, berdasarkan keseluruhan data yang tersedia. Berdasarkan hasil penelitian ini, metode K-Medoids terbukti sangat efektif dalam melakukan clustering pada data hasil panen tanaman hortikultura. Evaluasi kinerja algoritma dilakukan dengan memanfaatkan Davies-Bouldin Index (DBI) sebagai metode evaluasi, dilakukan pengukuran untuk menilai kualitas pengelompokan yang dihasilkan. Setelah proses evaluasi menggunakan DBI selesai, algoritma K-Medoids memperoleh nilai 0,5537744324187953.
Sentiment Analysis of User Reviews on BSI Mobile and Action Mobile Applications on the Google Play Store Using Multinomial Naive Bayes Algorithm Samudera, Brucel Duta; Nurdin, Nurdin; Aidilof, Hafizh Al Kautsar
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.581

Abstract

Mobile banking services are designed to facilitate customer transactions. Bank Syariah Indonesia (BSI) and Bank Aceh also provide these online services through their respective applications, BSI Mobile and Action Mobile. The mobile banking apps aim to simplify customer transactions, which can be conducted remotely via several features, from transfers, payments, and purchases to zakat payments, by simply connecting to the internet. Therefore, this research aims to classify the sentiment of user reviews for BSI Mobile and Action Mobile applications on Google Play Store to understand the users' experiences. The Multinomial Naïve Bayes algorithm is used in this study, where the algorithm analyzes and classifies the user reviews into positive and negative sentiment categories. The study involves several stages, such as text preprocessing, sentiment visualization, splitting the data into an 80:20 ratio for training and testing datasets, and training the model using the Multinomial Naïve Bayes algorithm. The results of this study show that the Multinomial Naïve Bayes algorithm performs well in analyzing user sentiment for BSI Mobile and Action Mobile, achieving an accuracy of 78.7%, precision of 76.5%, recall of 86.2%, and an F1-score of 80.6% for BSI Mobile, and an accuracy of 85.6%, precision of 75%, recall of 75%, and an F1-score of 75% for Action Mobile. Additionally, the sentiment classification results reveal that 52.8% of BSI Mobile user reviews are positive and 47.2% are negative, while for Action Mobile, 35.1% are positive and 64.9% are negative. For BSI Mobile, 21,497 reviews express a positive sentiment with dominant keywords such as "updated," "good," "balance," "transaction," and "thank." Meanwhile, for Action Mobile, 274 reviews express a negative sentiment with dominant keywords such as "transaction," "application," "network," "register," "please," and "update."
Comparison of Triple Exponential Smoothing and ARIMA in Predicting Cryptocurrency Prices Prasetyo, Adi; Nurdin, Nurdin; Aidilof, Hafizh Al Kautsar
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.577

Abstract

Cryptocurrency has emerged as a prominent digital asset over the past decade, but its high price volatility presents significant challenges for investors. This study evaluates and compares the effectiveness of the Triple Exponential Smoothing (TES) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting the prices of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Solana (SOL), and Ripple (XRP). TES models trends and seasonality in time series data, while ARIMA captures autoregressive patterns and moving averages. The dataset is split into 80% for training and 20% for testing, with performance evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). TES outperforms ARIMA in predicting Bitcoin and Binance Coin, achieving MAPE values of 10.38% and 13.81%, and RMSE values of 3,985.55 and 41.28, respectively. However, ARIMA shows better performance for Ethereum, Solana, and Ripple, with MAPE ranging from 8.78% to 32.84% and RMSE between 0.08 and 204.59. Notably, Ethereum has the lowest MAPE at 8.78%, while Ripple exhibits the smallest RMSE at 0.08. These findings suggest that TES is more suitable for cryptocurrencies with relatively stable price patterns, while ARIMA is better adapted to forecasting highly volatile assets. This research underscores the importance of selecting forecasting models based on the specific characteristics of each cryptocurrency
Developing Prototype Model Based on Analysis of The People at The Center of Mobile App Development (PACMAD) on The Panel Harga Pangan Application Fitria, Rahma; Meiyanti, Rini; Aidilof, Hafizh Al Kautsar; Ruzanna, Arina; Hamsi, Widia; Na'syakban, Irvan
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3115

Abstract

The increasing demand on mobile applications to monitor and analyze market trends in the food industry necessitates a focus on usability to ensure that these tools are functional and user-friendly. The Panel Harga Pangan app, which is widely utilized by traders, consumers, and policymakers, provides essential information on food prices across many marketplaces. However, as its user base grows, correcting usability concerns becomes increasingly important to its sustained effectiveness and customer happiness. This article looks into the implementation of the People At The Center Of Mobile Application Development (PACMAD) usability concept on the panel harga pangan app and finally proposed the prototype to similar application. The PACMAD model, designed specifically for mobile applications, evaluates usability based on seven key criteria: effectiveness, efficiency, satisfaction, learnability, memorability, mistakes, and cognitive load. The application's effectiveness is approximately (62.5%), including efficiency (73.27%), satisfaction (64%), learnability (65.78%), memorability (70.93%), errors (68.59%), and cognitive load (72.72%). The study's findings show that the application has an average score of 68%, indicating that the program is neither particularly successful or satisfying. Issues such as less efficiency and higher error frequency diminish the overall user experience. The research includes specific recommendations for improving the app's usability, such as redesigning the user interface and optimizing onboarding processes. These findings aim to improve the user experience, ensuring that the panel harga pangan remains a reliable and user-friendly tool for its varied audience. The findings have significant consequences for applying the PACMAD model to other mobile agriculture applications.
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.
Co-Authors A, Hendra Adi Prasetyo Al-Shahzam , Mohd. Hafez Angelina, Difa Azwir, Andrea Micola Eri Saputra Fachruzi, Faza Reihan Fakhrurrazi Habib Muharry Yusdartono Hamsi, Widia Hisyam, Mochammad Iswari, Syahyana Kurniawati Kurniawati Lidya Rosnita Lubis, Aulia Azzahra Ma'aruf Muhammad Ikhwani Muhammad Muaz Munauwar Na'syakban, Irvan Nurdin Nurdin Qardawi, Muhammad Yusuf Rahma Fitria, Rahma Rangkuti, Haris Yunanda Rini Meiyanti Rizal S.Si., M.IT, Rizal Rozzi Kesuma Dinata Ruzanna, Arina Sahputra, Ilham Samudera, Brucel Duta Veri Ilhadi Zahratul Fitri, Zahratul Zohra, Siti Fatimah A