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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Hasil Pengabdian Kepada Masyarakat Martabe : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Education and Mathematical Science Journal of Applied Engineering and Technological Science (JAETS) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Instal : Jurnal Komputer sudo Jurnal Teknik Informatika Hello World Blend Sains Jurnal Teknik Innovative: Journal Of Social Science Research Jurnal Pengabdian Barelang Hanif Journal of Information Systems Portal Riset dan Inovasi Sistem Perangkat Lunak Tsabit PAEDAGOGIE Inventa: Journal of Science, Technology, and Innovation
Halim Maulana
Universitas Muhammadiyah Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Design and Construction of Automatic Clothes Drying Rack Prototype Based on IoT (Internet of Things) Syahputra, Abdillah; Maulana, Halim
Tsabit Journal of Computer Science Vol. 1 No. 2 (2024): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit31

Abstract

During the dry season, the intense heat of the sun is highly sought after by Indonesian communities for various needs, one of which is drying clothes that are still wet. Therefore, Indonesians prefer using clotheslines as a medium for drying clothes. Essentially, in addressing the issue of clotheslines, an automated control system is needed. Advances in the field of science and technology, particularly in IoT (Internet of Things), will lead to new innovations. One such innovation is an automatic clothesline control system, which helps and simplifies human tasks. The system automatically moves or shifts dried clothes to a place that is not exposed to rain. In this research, an automatic clothesline system is designed to secure clothes during rain or other weather changes using several sensors: rain sensor, Light Dependent Resistor (LDR), and Temperature and Humidity Sensor (DHT), as well as an external fan that functions as additional drying assistance during rain. This system utilizes the ESP8266 microcontroller and is based on the Internet of Things, allowing remote monitoring and control via smartphone. Based on conducted tests, this system effectively responds to weather changes.
Smart Blind Stick Design Using HC-SR04 Sensor and ESP 32 Based Water Level Sensor to Improve the Mobility of Blind Persons Zakhir, Zharfan; Maulana, Halim
Tsabit Journal of Computer Science Vol. 1 No. 2 (2024): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit32

Abstract

Blind people in Indonesia, who are estimated to number around 3.75 million people, face major challenges in their daily mobility. In the era of technology 4.0, various innovations have been developed to help them, including the use of walking aids such as smart blind sticks. This research aims to design and build a smart blind stick based on the ESP32 microcontroller, which is equipped with an HC-SR04 ultrasonic sensor and water level sensor to detect holes and puddles of water, as well as a vibration module and speaker to provide warnings. The research method used is the prototyping method, which involves collecting system requirements, making prototypes, and evaluating users. The research results show that this smart blind stick is effective in providing warnings of obstacles on the road through vibration and sound, as well as making travel easier and increasing the safety of blind people. All main components function as expected, making this device a practical and innovative solution for improving the mobility of blind people.
Comparison of Random Forest and XGBOOST Methods on Weather in North Sumatera Sibuea, Royhan Umri; Maulana, Halim
Tsabit Journal of Computer Science Vol. 2 No. 1 (2025): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit44

Abstract

Accurate weather forecasting is crucial for various sectors, including agriculture, transportation, and disaster management. The weather data used includes variables such as humidity, temperature, and wind speed collected from weather stations across North Sumatra. The Random Forest method is an ensemble algorithm based on decision trees known for its ability to handle overfitting and provide accurate results. On the other hand, XGBoost is a boosting technique that improves model performance through iterative learning, correcting errors made by previous models. Research results show that both methods have their respective advantages in terms of accuracy and prediction speed. The Random Forest method yields a Root Mean Squared Error (RMSE) of 0.753732 and a Coefficient of Determination (R²) of 0.736315. In contrast, XGBoost shows a slightly lower RMSE of 0.737818 and a higher R² of 0.747332. It is concluded that XGBoost performs slightly better in minimizing prediction errors (RMSE) and improving model fit to the data (R²) compared to Random Forest.
Penerapan Algoritma DBSCAN dan XGBoost untuk Menganalisis Keberhasilan Pembelajaran Bahasa Indonesia bagi Penutur Asing (BIPA) di Songsermsasana School, Hat Yai Rahma, Nadya; Maulana, Halim
Paedagogie Vol 21 No 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/paedagogie.v21i1.16185

Abstract

Traditional evaluation of Bahasa Indonesia for Foreign Speakers (BIPA) learning success relies on subjective teacher assessments lacking objectivity. This study aims to integrate DBSCAN and XGBoost algorithms to analyze learning patterns and dominant success factors in BIPA. Quantitative Educational Data Mining (EDM) exploratory approach applied to 200 students population at Songsermsasana School, Hat Yai, Thailand during 27-day KKN, using complete tabular data sample. Instruments include activity scores, class participation, attendance, exam/quiz scores, study time, and question frequency variables; analysis techniques involve preprocessing, DBSCAN clustering (ε=0.5, minPts=5), and XGBoost feature importance. Results reveal three clusters: Cluster 0 (high speaking/writing >80), Cluster 1 (stable receptive skills), Cluster 2 (low attendance), with speaking score (15.89%) and writing score (11.61%) dominant. Hybrid model outperforms K-Means in handling noise. Research provides objective data-driven evaluation for global BIPA teaching personalization.
Visual Detection of Oil Palm Maturity Leveraging Simple Evolving Connectionist System Al-Khowarizmi Al-Khowarizmi; Fatma Sari Hutagalung; Halim Maulana
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/kvqm6450

Abstract

Detecting the ripeness of oil palm fruit bunches is a crucial process in the palm oil industry to ensure the quality and quantity of oil extracted. Conventional methods still rely on subjective and inefficient manual observation. This study proposes a visual detection system using the Simple Evolving Connectionist System (SECoS) algorithm to identify the ripeness of oil palm bunches based on visual images. This model utilizes color, texture, and shape characteristics extracted from images and processed through an adaptive and evolving neural network structure. The results demonstrate that SECoS is capable of high detection accuracy and adapts to new data patterns. This system has the potential to be applied in precision agriculture practices. The model achieved an average accuracy of 91.3%, with the highest accuracy of 94% in the "Ripe" category in the final test based on 300 dataset. This demonstrates that parameter optimization is crucial in improving the model's ability to adapt to variations in oil palm bunch image data. Accuracy improvements were evident in both training and validation data. However, not all categories achieved optimal results, with accuracy for the "empty bunch" labels (89%) and "unripe" labels (88%) being relatively lower than for the other categories.
Klasifikasi BAU Dalam Kulkas Menggunakan Sensor SGP-30 Dan Algoritma Random Forest Classifier Amira Muhammad Salim Banem; Halim Maulana
Hello World Jurnal Ilmu Komputer Vol. 4 No. 4 (2026): Edisi Januari
Publisher : Ilmu Bersama Center

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Abstract

Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi bau dalam kulkas menggunakan sensor SGP-30 dan algoritma Random Forest Classifier. Sistem ini dirancang untuk mendeteksi dan mengklasifikasikan kondisi bau makanan ke dalam tiga kategori, yaitu Segar, Sedang, dan Busuk, berdasarkan data sensor berupa nilai eCO₂, TVOC, suhu, dan kelembapan. Data yang diperoleh kemudian diproses menggunakan algoritma Random Forest Classifier yang mampu menghasilkan akurasi sebesar 97,78% dalam melakukan klasifikasi bau. Hasil penelitian menunjukkan bahwa sistem yang dibangun dapat mengidentifikasi kondisi bau secara akurat dan efektif. Sistem ini berpotensi untuk dikembangkan lebih lanjut dengan memperluas variasi data dan mengeksplorasi algoritma klasifikasi lainnya guna meningkatkan performa sistem.
Web-Based Gradient Boosting Machine Implementation for Student Success Data Classification at Muhammadiyah Elementary School in East Medan Afdolly Akbar Khaidir Siregar; Halim Maulana
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit63

Abstract

The rapid advancement of data-driven education has enabled schools to utilize machine learning to identify factors influencing student success. This study presents the development and implementation of a web-based Gradient Boosting Machine (GBM) model for classifying student success data at Muhammadiyah Elementary School in East Medan. The proposed system aims to assist educators in evaluating student performance through predictive analytics that integrates academic, behavioral, and attendance data. The research methodology includes data preprocessing, feature selection, and model training using the GBM algorithm due to its robustness in handling non-linear relationships and reducing classification errors through iterative boosting. The web-based application is designed with an interactive interface, allowing teachers and administrators to input, analyze, and visualize student performance patterns easily. The evaluation results indicate that the GBM model achieves high classification accuracy, outperforming traditional algorithms such as Decision Tree and Logistic Regression. This system not only provides accurate predictions of student performance levels but also generates actionable insights for improving learning outcomes and academic interventions. The research contributes to the integration of machine learning and educational management by demonstrating how predictive modeling can be operationalized in real-time through a web-based platform to support data-informed decision-making in Muhammadiyah schools.
Optimization of support vector machine with cubic kernel function to detect cyberbullying in social networks Al-Khowarizmi Al-Khowarizmi; Indah Purnama Sari; Halim Maulana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.25437

Abstract

Social networking is a place where humans can interact using the internet network to be able to disseminate information, discuss, exchange ideas, pour out their hearts, and share activities. Many social networks are popularly used, one of which is Twitter. Information can be received quickly using Twitter. In addition, various government agencies also use Twitter to be able to interact directly with the community so that every government policy is disseminated through this social network. Every government policy neglects to reap the pros and cons of society, both collectively and individually. As a result of the pros and cons, a trial called cyberbullying was recorded. Cyberbullying in various studies has been carried out to change a person’s raw material so that with the application of information technology, identifying cyberbullying needs to be carried out further. The problem of cyberbullying is generally detected using the support vector machine (SVM) method. Cyberbullying detection is conducted in dealing with government policy data such as “cipta kerja” by using the SVM method which is optimized using the cubic kernel function. The accuracy value achieved in SVM uses a linear kernel function of 92.3% while using a cubic linear function of 90%.
IoT Based Industrial Waste Monitoring System Design with Data Visualization on A Web Application Using The Supervised Learning Method Muhammad Nauval Asyqar Ridwan Ritonga; Halim Maulana
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.50

Abstract

Industrial waste management is a critical aspect of sustainable manufacturing, as improper handling can lead to severe environmental pollution and health hazards. Real-time monitoring of industrial waste parameters enables early detection of irregularities and supports informed decision-making for compliance with environmental regulations. This study presents the design of an IoT-based industrial waste monitoring system integrated with data visualization on a web application and enhanced by the supervised learning method for predictive analysis. The system utilizes IoT sensor nodes to measure key waste parameters such as pH level, temperature, turbidity, and chemical concentration. Sensor data is transmitted wirelessly to a cloud server, where it is stored, processed, and analyzed using supervised learning algorithms to classify waste quality and detect potential violations. The web application provides interactive dashboards, historical data tracking, and real-time alerts for stakeholders. Testing results demonstrate that the system achieves high accuracy in classifying waste conditions, offers user-friendly visual analytics, and enables proactive waste management. This research contributes to the development of intelligent environmental monitoring solutions, promoting efficiency, compliance, and sustainability in industrial operations.
SEO (Search Engine Optimization) Finding Growth Opportunities in Organic Traffic on the KAIA Media.id Website Yoga Pramana; Halim Maulana
Hanif Journal of Information Systems Vol. 3 No. 2 (2026): February Edition
Publisher : Ilmu Bersama Center

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Abstract

The rapid growth in digital marketing highlights the importance of Search Engine Optimization (SEO) in enhancing website visibility and attracting organic traffic. This research focuses on SEO optimization for Kaia Media.id by developing a dedicated Keyword Tool Suggestion application. The primary goal is to identify opportunities to increase organic traffic to the Kaia Media.id website by efficiently targeting relevant keywords. The developed tool helps discover high-potential keywords that align with the interests and search behavior of the target audience. The Keyword Tool Suggestion application provides an easy-to-use interface to generate effective keyword suggestions based on current trends and search engine algorithms. By integrating this tool into Kaia Media.id's SEO strategy, the website can improve its search engine ranking, attract more visitors, and ultimately enhance its online presence. The application is tested to ensure that it provides accurate and actionable keyword suggestions, facilitating more effective content creation and marketingstrategies. By leveraging the Keyword Tool Suggestion application, Kaia Media.id aims to position itself as a leading digital marketing agency, offering high-quality SEO and online marketing solutions to its clients. The findings of this research affirm the value of targeted keyword optimization in achieving sustainable growth in organic traffic.
Co-Authors Afdolly Akbar Khaidir Siregar Al Khowarizmi Al Khowarizmi Amira Muhammad Salim Banem Anitya Jauza Rachmad Edy Rahman Syahputra Efendi, Raimon Efendi, Syahril Farhan Al Ikhsan Farida Nafisa Siagian Fatma Sari Hutagalung Ferdy Riza Gusrion, Deval Indah Purnama Sari Irvan, Irvan Juliaz, Deal Alfi Lubis, Husni Lutfi Basit Marah Doly Nasution Mhd. Basri Mintahul Rizka Nasution Muhammad Nauval Asyqar Ridwan Ritonga Mulkan Azhari Nasution, Chairul Ichwan Purwanto Purwanto Rahma, Nadya Rahmad Wahyudi Raveah Nasution Sari Hutagalung, Fatma Sari, Ayu Sekar Sefhia Astri Yulika Purba Sibuea, Royhan Umri Siti Nur Hidayah, Siti Nur Suryana, Febriyanno Syahputra, Abdillah Tanjung, M. Iqbal Yoga Pramana Zakhir, Zharfan Zaki Zain Rangkuti