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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
Water Quality Monitoring and Control System for Tilapia Cultivation Based on Internet of Things Rosnita, Lidya; Ikhwani, Muhammad; Aidilof, Hafizh Al Kautsar; Salamah, Salamah; Hamsi, Widia; Rangkuti, Haris Yunanda
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.566

Abstract

This research analyzes the quality of water for tilapia habitat which is a type of brackish water fish that is currently widely cultivated by pond farmers. This fish is the choice because of its flexibility regarding habitat. However, despite having flexibility in terms of habitat, each harvest of tilapia that lives in a different habitat will produce tilapia with different quantity and quality. Currently, many tilapia farmers still carry out the cultivation process using traditional methods using ponds. Kuala Kerto Village, Lapang District, North Aceh is one of the locations where many tilapia fish farmers use ponds as a habitat for this fish. Not infrequently, changes in natural conditions such as rain and floods have an impact on tilapia fish ponds in this village. Thus, crop yields are very varied, often even resulting in losses. One of the reasons for this is that there is still minimal use of technology in tilapia cultivation in this village. The design of a water quality monitoring and control system for IoT-based tilapia cultivation in this research was carried out to help the problems of tilapia pond farmers. Through this research, a tool was produced in the form of a prototype IoT device that can be used to monitor and control water quality in tilapia fish ponds. This device utilizes several sensors such as turbidity sensors, ammonia sensors, salinity sensors, pH sensors, and several other sensors as data takers which will later be transmitted and displayed via a web application. Research and development of this device uses the RD method, namely research and development.
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.
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.
Co-Authors A, Hendra Adi Prasetyo Al-Shahzam , Mohd. Hafez Angelina, Difa 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 Muhammad Ikhwani Muhammad Muaz Munauwar Munawarah Munawarah, Munawarah Na'syakban, Irvan Nurdin Nurdin 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