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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) JURNAL PENDIDIKAN TAMBUSAI JURIKOM (Jurnal Riset Komputer) TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Pengabdian Masyarakat Bhinneka Jurnal Abdimas Maduma LOGIC : Jurnal Ilmu Komputer dan Pendidikan OKTAL : Jurnal Ilmu Komputer dan Sains BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat Riau Jurnal Teknik Informatika
Yuda Samudra
Universitas Pamulang
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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ANALISIS KOMPARATIF METODE DATA MINING MULTISEKTOR PADA DATASET COVID-19, SAHAM BEI, DAN PERUSAHAAN GLOBAL Fajriansyah, Satria Nur; Pramadhan, Harsya Rafif; Safa, Alaudin; Nurfajriansyah, Dandy; Wijaya, Muhammad Subaktiar; Samudra, Yuda
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 5, No 2 (2025): JURNAL JRIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol5no2.903

Abstract

The rapid advancement of information technology has significantly driven the adoption of data mining techniques across various sectors, primarily to uncover hidden patterns and support data-driven decision-making processes. This study aims to analyse and compare the effectiveness of several data mining methods—namely K-Means Clustering, Decision tree, Principal component analysis (PCA), and Bootstrapping—in processing datasets from three distinct domains: public health (COVID-19), finance (Indonesia Stock Exchange), and global business (multinational corporations). The datasets utilised include COVID-19 data sourced from Kaggle, stock data listed on the Indonesia Stock Exchange, and corporate data comprising industry classifications and revenue attributes of global companies. The methodology adopted in this research encompasses several critical phases: data preprocessing to ensure consistency and reliability; implementation of classification and clustering algorithms; and model evaluation through accuracy metrics and visual analytics. Findings indicate that the K-Means algorithm performs effectively in clustering both COVID-19 spread regions and stock data based on numerical features. The Decision tree method demonstrates strong predictive capabilities in classifying risk categories within both COVID-19 datasets and corporate profiles. PCA proves to be valuable in reducing data dimensionality while retaining essential information. Furthermore, Bootstrapping is employed to enhance the generalizability of the models, particularly in scenarios involving limited data samples. The study concludes that integrating multiple data mining approaches can yield comprehensive insights across sectors, although the level of effectiveness varies depending on the inherent characteristics of each dataset. Such a multidisciplinary and combined approach provides a robust framework for data-driven analysis and strategic decision support in diverse fields.Kemajuan pesat dalam teknologi informasi telah memperluas pemanfaatan teknik data mining di berbagai bidang, khususnya dalam mengidentifikasi pola tersembunyi dan menunjang proses pengambilan keputusan berbasis data. Penelitian ini secara khusus mengkaji dan membandingkan efektivitas empat pendekatan data mining yakni K-Means Clustering, Decision tree, Principal component analysis (PCA), dan Bootstrapping, dalam mengolah data yang berasal dari tiga sektor strategis: sektor kesehatan (terkait COVID-19), sektor keuangan (pasar saham BEI), dan sektor bisnis global (perusahaan multinasional). Dataset yang digunakan bersumber dari berbagai platform terpercaya, termasuk data COVID-19 dari Kaggle, data saham perusahaan yang terdaftar di Bursa Efek Indonesia, serta informasi perusahaan multinasional yang mencakup variabel industri dan pendapatan tahunan. Rangkaian metodologi penelitian diawali dengan proses prapengolahan data (data preprocessing) untuk memastikan kualitas dan konsistensi data, dilanjutkan dengan penerapan algoritma klasifikasi dan pengelompokan (clustering), serta evaluasi performa model menggunakan metrik akurasi dan representasi visual. Dari hasil analisis yang dilakukan, ditemukan bahwa algoritma K-Means menunjukkan performa yang baik dalam mengelompokkan wilayah berdasarkan tingkat penyebaran COVID-19 serta dalam mengklasifikasikan saham berdasarkan indikator numerik. Sementara itu, metode Decision tree terbukti efektif dalam memprediksi kategori risiko, baik dalam konteks data kesehatan maupun data korporasi multinasional. PCA turut berkontribusi signifikan dalam mereduksi dimensi data tanpa kehilangan informasi utama yang relevan. Selain itu, teknik Bootstrapping diaplikasikan untuk meningkatkan kemampuan generalisasi model, terutama saat berhadapan dengan keterbatasan jumlah data. Secara keseluruhan, temuan penelitian ini menegaskan bahwa pendekatan kombinatif dalam data mining dapat menghasilkan wawasan mendalam yang lintas sektoral, dengan efektivitas yang bergantung pada karakteristik dan struktur data yang dianalisis. Pendekatan integratif semacam ini berpotensi memperkaya pemahaman dan mendukung pengambilan keputusan strategis di berbagai domain
Rancang Bangun Alat Otomatis Pengganti dan Pengontrol Air dengan Deteksi Tingkat Kekeruhan dan PH Pada Akuarium Ikan Cupang Sitio, Sartika Lina Mulani; Nardiono; Samudra, Yuda
Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Vol. 13 No. 2 (2025): TELEKONTRAN vol 13 no 2 Oktober 2025
Publisher : Program Studi Teknik Elektro, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/telekontran.v13i2.16690

Abstract

Water quality that is not optimally maintained can have a negative impact on the health of ornamental fish, especially Bluerim betta fish that require an aquarium environment with a certain level of acidity and turbidity of the water. The problems that are often faced by ornamental fish hobbyists are delays in changing water and difficulties in monitoring water conditions manually. This study aims to design and build an automatic water replacement and control system in betta fish aquariums with the ability to detect turbidity levels and water pH in real-time. The system uses a pH-SEN0161 sensor to measure acidity, a turbidity-SEN0189 sensor to detect turbidity in NTU units, and an SRF05 ultrasonic sensor to measure water level. The software was developed using the Arduino IDE and implemented on the Arduino ATMega2560 microcontroller as well as the NodeMCU ESP8266 for data processing and automatic control. The test was carried out for 30 days with an ideal pH standard between 6–7 and a turbidity value below 400 NTU. The test results show that the system can work optimally in replacing and controlling the water conditions of the Bluerim betta fish aquarium, thus supporting the quality of life of the fish effectively and efficiently.
Pemanfaatan Artificial Inteligence Dalam Pembuatan Video Pembelajaran di SMA Alia Islamic School Lely Panca Andriyanto; Nanang; Yuda Samudra
JURNAL ABDIMAS MADUMA Vol. 4 No. 2 (2025): Juli 2025
Publisher : English Lecturers and Teachers Association (ELTA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/jam.v4i2.476

Abstract

Di era digital saat ini, pemanfaatan teknologi, khususnya Artificial Intelligence (AI), menjadi penting untuk menciptakan media pembelajaran yang efektif dan menarik. Namun, di SMA Alia Islamic School, pemanfaatan AI masih terbatas karena guru cenderung menggunakan metode konvensional dan kurang memahami teknologi digital. Kegiatan pengabdian masyarakat ini bertujuan meningkatkan kompetensi profesional guru melalui pelatihan penggunaan AI dalam pembuatan video pembelajaran. Program mencakup pengenalan konsep dasar AI dalam pendidikan, pelatihan penggunaan platform Pictory AI, serta strategi integrasi teknologi dalam pembelajaran. Metode pelatihan meliputi ceramah, diskusi interaktif, dan praktik langsung untuk memberikan pengalaman menyeluruh. Selain meningkatkan keterampilan teknis, kegiatan ini juga mendorong motivasi guru agar lebih terbuka terhadap inovasi digital. Diharapkan, pelatihan ini dapat menghasilkan peningkatan kemampuan guru dalam membuat video pembelajaran berbasis AI yang menarik dan relevan, sekaligus membangun semangat untuk mengembangkan metode pembelajaran yang inovatif. Pemanfaatan AI diharapkan mampu meningkatkan kualitas pembelajaran di SMA Alia Islamic School dan memberikan dampak positif terhadap motivasi serta pemahaman siswa dalam menerima materi ajar Kata Kunci : Artificial Intelligence; video pembelajaran: Aplikasi Pictory AI: pelatihan guru
Rancang Bangun Sistem Aplikasi Ujian Akhir Sekolah Berbasis Jaringan Client Server Menggunakan Topologi Bus Samudra, Yuda
Riau Jurnal Teknik Informatika Vol. 4 No. 2 (2025): Juli 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i2.3415

Abstract

The implementation of the final school exam at SMK Al Amanah has so far been carried out manually using paper, which causes various problems such as wasting time, risk of losing documents, and difficulties in the process of correction and recapitulation of grades. These problems encourage the need for the application of information technology in supporting learning evaluation activities. This research aims to design and build a client-server network-based school exam system with an efficient and affordable bus topology. This system was developed to increase the effectiveness of the exam and minimize errors in the assessment process. The methods used in this study include the stages of system design, software development using the PHP programming language and MySQL database, and the implementation of local networks using a simple and cost-effective bus topology. The system consists of a single main server that manages questions and exam result data, as well as several client computers that are used by students to work on questions simultaneously. The test results show that the system can run well in a local network environment with a bus topology, where students can access the questions and take the exam simultaneously without any significant obstacles. In addition, the time of correction and recap of values becomes faster and more accurate. With this system, the exam process at SMK Al Amanah becomes more efficient, safe, and organized.
Predicting Student Academic Performance Using Learning Activity Data: A Comparative Study of Random Forest and Decision Tree Models Hidayat, Rahmat; Herwis Gultom; Samudra, Yuda
Riau Jurnal Teknik Informatika Vol. 4 No. 3 (2025): November 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i3.4032

Abstract

This study compares the effectiveness of the Random Forest and Decision Tree algorithms in predicting students' academic performance based on learning activities. The data used included reading scores, writing scores, math scores, and demographic variables such as gender, race/ethnicity, parental level of education, lunch, and test preparation course. The research was carried out through the stages of data cleaning, training and test data sharing, model training, and evaluation using confusion matrix and accuracy, precision, recall, and F1-score metrics. The results show that Random Forest performs best with 97% accuracy, surpassing Decision Tree which has 94% accuracy. The feature importance analysis  revealed that cognitive ability—especially in the reading score, writing score, and math score features—had the greatest influence on prediction results. These findings confirm that the Random Forest model is more reliable and effective as a prediction tool in the academic decision support system to detect the potential for decline in student achievement early.
Implementasi Teknik Integrasi Ms Excel Ms Word Dengan MailMerge Untuk Otomatisasi Generate Template Sertifikat Samudra, Yuda; Suhendri, Ade Putra Prima; Andriyanto, Lely Panca
Jurnal Pengabdian Masyarakat Bhinneka Vol. 4 No. 3 (2026): Bulan Februari
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v4i3.1094

Abstract

Dalam lingkungan pendidikan, kegiatan administrasi seperti pembuatan sertifikat pelatihan, penghargaan, maupun partisipasi kegiatan menjadi aktivitas rutin. Proses manual memakan waktu lama, meningkatkan risiko kesalahan ketik (human error), tidak efisien dalam jumlah sertifikat yang banyak. SMA Alia Islamic School akan melakukan kegiatan Class Meeting, selaku panitia dan anggota OSIS ingin memberikan penghargaan terhadap peserta maupun para juara pada setiap lomba secara masal serta pelatihan kepada siswa/siswi kelas XII untuk persiapan memasuki dunia kerja dalam bidang administrasi pengelolaan data serta pembuatan template otomatis. Solusi yang dapat diterapkan dengan memanfaatkan fitur MailMerge, teknik integrasi antara Ms.Excel dan Ms.Word untuk menghasilkan dokumen otomatis dalam jumlah banyak dengan format yang seragam. Melalui MailMerge, data peserta disimpan dalam file Ms.Excel sebagai sumber data, kemudian dihubungkan dengan template sertifikat di Ms.Word. Prosedur kegiatan dimulai dari Koordinasi Dengan Pihak Alia Islamic School, Persiapan, Pelaksanaan Penyuluhan dan Pelatihan. Tujuan kegiatan ini mengurangi waktu pembuatan sertifikat hingga 80% serta peningkatan kompetensi minimal 85% mampu menerapkan MailMerge secara mandiri. Kekurangan utama adalah pemahaman dasar peserta tentang fitur MailMerge sebelum pelatihan dimulai yang terlihat dari hasil Pre-Test. Beberapa peserta tidak familiar. Hal ini diatasi dengan menyediakan materi tambahan tentang solusi masalah umum MailMerge yang terbukti meningkatkan pemahaman peserta berdasarkan hasil Post-Test.
COMPARISON OF DECISION TREE AND NAIVE BAYES METHODS FOR RAINFALL CLASSIFICATION USING A WEATHER DATASET WITH A WEB-BASED APPLICATION Samudra, Yuda; Hidayat, Amin; Nanang
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/fxnw2631

Abstract

Rainfall prediction is an important component of weather analysis as it provides valuable information to support decision-making in sectors such as agriculture, transportation, and environmental management. Although various studies have compared machine learning algorithms for rainfall classification, many of them lack detailed discussion on dataset characteristics and practical system implementation. Therefore, this study aims to evaluate and compare the performance of Decision Tree and Naive Bayes algorithms for rainfall classification while considering dataset characteristics and implementing the model in a web-based application. The dataset used in this study consists of 2,500 records with meteorological parameters including temperature, humidity, wind speed, cloud cover, and atmospheric pressure. The data underwent preprocessing, including data cleaning and label encoding, where rainfall was represented as 1 and no rainfall as 0. The dataset was divided into training and testing sets, and both algorithms were applied to build classification models. Model performance was evaluated using confusion matrix, accuracy, and ROC curve analysis. The results show that the Decision Tree algorithm achieved an accuracy of 1.00 (100%), while the Naive Bayes algorithm achieved 0.972 (97.2%). Although Decision Tree shows superior performance, the perfect accuracy may indicate potential overfitting, and therefore the results should be interpreted carefully. Furthermore, the developed models were successfully implemented into a web-based application that enables users to perform rainfall prediction interactively. This study demonstrates that Decision Tree provides better performance for rainfall classification in the given dataset, while also highlighting the importance of considering dataset characteristics and evaluation methods. The integration of machine learning models into a web-based system provides a practical contribution for real-world weather prediction applications.   Keywords: Rainfall Classification, Decision Tree, Naive Bayes, Machine Learning, Weather Dataset, Web-Based Application
Predict Goods Demand Using the XGBoost Method Based on Sales Historical Data Badriah Nursakinah; Nurhalimah; Yuda Samudra
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9584

Abstract

Predicting the demand for goods is an important aspect of inventory management and operational planning because inaccurate predictions can lead to overstock or shortages of goods. This study aims to predict the demand for goods using the Extreme Gradient Boosting (XGBoost) algorithm based on historical sales data. The dataset used contains information on the transaction date, number of sales, stock, price, and time index, which is then processed through the preprocessing and feature engineering stages, including the formation of temporal features and sales lag features. Data sharing is carried out using a time series split approach to maintain the chronological order of the data. The XGBoost model is optimized using GridSearchCV with the TimeSeriesSplit validation scheme. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results showed that the model produced an MAE score of 54.13 and an RMSE of 77.60, while a SMAPE score of 43.13% showed an acceptable relative error rate in highly fluctuating sales data. Feature importance analysis shows that previous period (lag_1) sales and weekly patterns are the most dominant factors in demand predictions. These results prove that XGBoost is effectively used for historical data-driven demand prediction of goods and has the potential to support inventory management decision-making.
Pengenalan Cyber Security Sebagai Fundamental Keamanan Data Pada Era Digital Yuda Samudra; Amin Hidayat; Meidy Fajar Wahyu
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 12 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

Cyber ​​crime is a relatively new crime, which is carried out by people who are experts or who have expertise in the field of computers and information technology. When viewed from the perspective of crime, crime through cyberspace (internet) can have an impact inside and outside the virtual world. The limited space and time in carrying out activities using the internet as a medium makes it difficult for an activity in cyberspace to be detected conventionally. In the case of cyber threats, based on data analysis of the ID-SIRTII (Indonesian Security Incident Response Team On Internet Infrastructure) traffic monitoring system, it was noted that the incidence of cyber attacks in Indonesia has reached one million incidents and will increase every day due to system and application weaknesses that do not exist. is known. Protecting digital assets is an important concern, as cyber attacks affect a company's performance and reputation. Many methods and techniques can be used to support the process of directing students to take advantage of great opportunities with the impact of the development of internet technology. One of the introductions to Cyber ​​Security and how to enter personal data on gadget devices that currently students are good at using these devices, but still lacks knowledge about data security. With the introduction of cyber security as fundamental data security in the digital era, it is hoped that students will have an awareness of the importance of personal data so that data does not occur by irresponsible people so as to harm various parties whose data is misused. So, this Community Service Activity aims to provide direction to students by conducting face-to-face counseling with students to introduce the importance of data security in the digital era and support understanding of cyber security and cyber crimes that occur in the current era. This activity is in the form of training materials and practices during the training. The material contains a basic introduction to the impact of training on the advancement of internet technology, how to implement data security, and the application of safe e-commerce usage for personal data security. In preparation for the training, the service team tested the application of data in the use of e-commerce as material which was then discussed during the training. The training was given in the form of a lecture followed by a question and answer session. Starting from the introduction of the impact of post-advancement internet technology, then they will be taught how to apply security data to digital products when transacting online, so that the way the data is known by many people, then students will also be provided with practices on how to use security systems on gadgets and understand how to flow. secure transactions on websites, e-commerce and games. The training was held in the Computer Lab of Madrasah Aliyah Negeri 1, South Tangerang City, so that students could immediately practice what they were learning in this training. Keywords: Cyber Security, Keamanan Data, Era Digital, Data, Cyber Crime
Mobile Programming Untuk Siswa SMK Kesehatan Utama Insani Panongan Membuka Jendela Peluang Teknologi Dalam Dunia Kesehatan Ade Putra Prima Suhendri; Meidy Fajar Wahyu; Yuda Samudra
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 1 (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

In the current digital era, the integration of information technology has become crucial, especially in the healthcare sector. This activity highlights a mobile programming introduction program designed to enhance the technological understanding of students at the SMK Kesehatan Utama Insani Panongan. With the growing demand for skilled workers in mobile programming, the program aims to prepare students for an increasingly digital workforce. The program offers training on basic technological concepts, programming languages, and mobile application development processes. The hope is that participating students will gain a deep understanding and practical skills to compete and contribute in a healthcare sector that is becoming more integrated with technology. The benefits of this program are not limited to individual levels. With enhanced technological understanding, students have the potential to leverage technology in improving healthcare information management, patient monitoring, and health education. This means that the public can enjoy better and more efficient access to healthcare information. Moreover, the program fosters vital awareness of technology's role in the healthcare sector. It's not just about career opportunities but also about making meaningful contributions to the advancement of public health. Through this initiative, SMK Kesehatan Utama Insani Panongan strives to enhance the quality of student education and simultaneously make a positive contribution to the future progress of technology and healthcare. Overall, this activity underscores the urgency of equipping the younger generation with adequate technological understanding and skills to face the challenges of an increasingly digital and integrated workforce.
Co-Authors Ade Putra Prima Suhendri Amin Hidayat Amin Hidayat Andriyanto, Lely Panca Badriah NurSakinah Darmawati Fadilah, Muhammad Farhan Fajriansyah, Satria Nur Fizram, Muhammad Herwis Gultom Hizkia Bayu Wijaya Lely Panca Andriyanto Lely Panca Andriyanto Meidy Fajar Wahyu Meidy Fajar Wahyu Nanang Nanang Nanang nanang nanang Nardiono Nurfajriansyah, Dandy NURHALIMAH Oke Hariansyah Pramadhan, Harsya Rafif Rahmat Hidayat Safa, Alaudin Sartika Lina Mulani Suhendri, Ade Putra Prima Suryaningrat Wijaya, Muhammad Subaktiar Wiwin Winarti Zurnan Alfian