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All Journal TEKNIK INFORMATIKA TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal TIMES International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BIOLINK (Jurnal Biologi Lingkungan, Industri, Kesehatan) JURNAL MEDIA INFORMATIKA BUDIDARMA JTAM (Jurnal Teori dan Aplikasi Matematika) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science Pelita Masyarakat Journal of Applied Data Sciences JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Science in Information Technology Letters Journal of Applied Computer Science and Technology (JACOST) International Journal of Engineering, Science and Information Technology EXPLORER Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Pada Masyarakat METHABDI Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Jurnal Pengabdian Masyarakat (Jubdimas)
Hartono Hartono
Department Of Magister Program In Informatics, Postgraduate School, Universitas Medan Area
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation

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Penerapan Smart Farming Sebagai Upaya Modernisasi Pertanian Cabai Rahman, Sayuti; Indrawati, Asmah; Sembiring, Arnes; Hartono, Hartono; Zuhanda, Muhammad Khahfi; Ongko, Erianto
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 02 (2024): EDISI SEPTEMBER 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v6i02.1050

Abstract

Cabai merupakan salah satu komoditas hortikultura yang memiliki nilai ekonomi tinggi, namun produktivitasnya sering terganggu oleh berbagai penyakit daun yang disebabkan oleh hama, seperti bercak daun, layu fusarium, embun tepung, dan virus kuning. Penyakit-penyakit ini tidak hanya memengaruhi kualitas hasil panen, tetapi juga menyebabkan kerugian ekonomi yang signifikan bagi petani. Untuk mengatasi permasalahan ini, dilakukan pengabdian kepada masyarakat dengan mengimplementasikan teknologi Convolutional Neural Network (CNN) untuk klasifikasi penyakit daun cabai secara cepat dan akurat. Metode yang digunakan melibatkan observasi lapangan untuk mengidentifikasi permasalahan yang dihadapi petani di Desa Lubuk Cuik, Batu Bara, Sumatera Utara. Data berupa gambar daun cabai yang terinfeksi dikumpulkan dan digunakan untuk melatih model CNN. Model yang dikembangkan, efficientChiliNet, mampu mengklasifikasikan penyakit daun cabai dengan akurasi pelatihan 99,8% dan akurasi validasi 96,5%. Aplikasi berbasis web dan desktop kemudian dibuat untuk mempermudah petani dalam mendiagnosis penyakit daun cabai secara mandiri. Aplikasi ini juga disosialisasikan kepada petani melalui pelatihan untuk memastikan implementasi teknologi yang optimal. Hasil pengabdian ini menunjukkan bahwa teknologi berbasis CNN mampu memberikan solusi efektif dalam mengidentifikasi penyakit daun cabai dan membantu petani meningkatkan produktivitas pertanian. Rekomendasi selanjutnya adalah pengembangan fitur tambahan dalam aplikasi untuk memberikan panduan penanganan hama dan integrasi teknologi Internet of Things (IoT) untuk pemantauan lingkungan secara real-time. Dengan pendekatan ini, diharapkan terciptanya modernisasi pertanian berbasis smart farming yang berkelanjutan.
Bibliometric analysis of model vehicle routing problem in logistics delivery Zuhanda, Muhammad Khahfi; Hartono, Hartono; Sidik Hasibuan, Samsul Abdul Rahman; Abdullah, Dahlan; Gio, Prana Ugiana; Caraka, Rezzy Eko
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp590-600

Abstract

This bibliometric analysis focuses on the vehicle routing problem (VRP) model in the field of logistics delivery. The study utilizes a comprehensive dataset of 2,000 VRP-related publications obtained from the Scopus database, spanning the years 2007 to 2023. Through the application of bibliometric methods, this research aims to uncover key insights regarding research trends, country contributions, and recent topics within the VRP research network. Various bibliometric indicators, including publication count, author productivity, relevant sources, institutional affiliation, and citation frequency, are employed to conduct the analysis. The findings shed light on the evolution and trajectory of VRP research, while also highlighting noteworthy countries and topics that have received significant attention. This study not only enhances the overall understanding of VRP but also serves as a foundation for future investigations aimed at enhancing the efficiency and effectiveness of logistics delivery.
Recipient Feasibility Decision Support System Micro Small Medium Business Assistance Use Method Analytic Hierarchy Process and Simple Additives Weighting Abdullah, Dahlan; Erliana, Cut Ita; Bintoro, Andik; Hartono, Hartono; Ikhwani, Muhammad; Nazaruddin, Nazaruddin
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2321

Abstract

Study This aims To determine the eligibility of MSME assistance recipients with the method AHP (Analytic Hierarchy Process) And SAW (Simple Additives weighting). The AHP method is used to determine the weight of each criterion. Meanwhile, SAW is used To determine the rank selection of beneficiaries. This is very important for Indonesia's economy during the crisis, Where MSME's own Power stands to face a crisis economy. Criteria used in a way This uses six measures: type of business, Amount of power Work, turnover per month, amount of assets, sector MSME, And sector business. Decision support systems are designed to support someone who must make certain decisions. That is, interactive, Flexible, Data quality, and Expert Procedure. Study System Supporters Decision Appropriateness Recipient Help Business Micro Community Use Analytic Hierarchy Process (AHP) and Simple Additive Methods weighting (SAW), Study This done in Subdistrict Intersection Three Regency Pidie Aceh Province to facilitate the Selection of Eligibility of Government Assistance Recipients For Build a business Micro Society. Testing is done in this study, namely black box testing. Results Testing black box shows that the system can walk with Good by function, with results calculation method AHP and results calculation method SAW in determining eligibility selection MSME aid recipients. The results of the level of accuracy testing on the AHP and SAW methods with six criteria and alternatives the requirements is 75%.
Challenges and Strategies in Forensic Investigation: Leveraging Technology for Digital Security Using Log/Event Analysis Method Ammar Yasir Nasution; Hartono Hartono; Rika Rosnelly
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.42815

Abstract

Cybersecurity threats continue to evolve, necessitating advanced techniques for network anomaly detection. This study developed a comprehensive methodology for detecting network anomalies by leveraging sophisticated log and event analysis using machine learning algorithms. By employing a Naive Bayes classification approach on a synthetic cybersecurity dataset comprising 40,000 entries with 25 unique features, the research aimed to enhance anomaly detection precision. The methodology involved meticulous data preprocessing, feature selection, and strategic model validation techniques, including cross-validation and external benchmarking. Comparative analysis with K-Nearest Neighbors and Support Vector Machine algorithms demonstrated the Naive Bayes method's superior performance, achieving a classification accuracy of 94.8%, an Area Under the Curve (AUC) of 0.949, and a Matthews Correlation Coefficient of 0.896. The study identified critical parameters influencing anomaly detection, such as source port characteristics and attack signatures. These findings contribute significant insights into machine learning-based network security strategies, offering a robust framework for early threat identification and mitigation.
Impact of Adaptive Synthetic on Naïve Bayes Accuracy in Imbalanced Anemia Detection Datasets Zuhanda, Muhammad Khahfi; Lisya Permata; Hartono; Erianto Ongko; Desniarti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6031

Abstract

This research aims to analyze the impact of the Adaptive Synthetic (ADASYN) oversampling technique on the performance of the Naïve Bayes classification algorithm on datasets with class imbalance. Class imbalance is a common problem in machine learning that can cause bias in prediction results, especially in minority classes. ADASYN is one of the oversampling methods that focuses on adaptively synthesizing new data for minority classes. In this study, the performance of the Naïve Bayes algorithm was tested on Anemia Diagnosis datasets before and after the application of ADASYN. This dataset contains 104 instances, 5 attributes, and 2 classes, and has an imbalance ratio of 3. The evaluation was carried out by comparing accuracy, confusion matrix, precision, recall, and F1-score to obtain a more comprehensive picture of the effectiveness of ADASYN in improving Naïve Bayes. The results of the study show that the performance of the oversampling method depends on the imbalance ratio so it is important to ensure that the oversampling method does not cause overfitting and this can be overcome by using ADASYN which only selects Selected Neighbors. The results showed that ADASYN significantly increased accuracy from 0.57 to 0.78, precision from 0.17 to 0.74, recall from 0.20 to 0.88, and F1-Score from 0.18 to 0.80. In this study, we also compared the application of ADASYN and SMOTE on the Naïve Bayes algorithm. The results show that ADASYN outperforms SMOTE across all key metrics—accuracy, precision, recall, and F1-Score—while the accuracy improvements were statistically significant (p-value = 0.00903).
PARENTING DAN SEMINAR PENDIDIKAN KARAKTER SEBAGAI UPAYA PEMBENTUKAN KARAKTER SISWA SMP & SMA SEKOLAH ANGKASA LANUD SOEWONDO MEDAN Maria, Elvie; Manalu, Brilliant Handyman; Hartono, Hartono; Nadapdap, Kristanty M. N.; Sudarso, Andriasan; Pasaribu, Dompak
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 3 No 2 (2023): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol3No2.pp217-225

Abstract

The acts of crime and delinquency among children and teenagers continue to increase, such as robbering, mugging, stealing, rape, brawls, bullying and even murder, as well as other acts of violence, as well as their mental health conditions becoming increasingly worrying, which is marked by an increase in suicide attempts and suicides. Therefore, there needs to be cooperation from both of the government and society, by looking at the psychological side of the individual perpetrator, the parenting style of the family, community and society at large so that similar acts do not continue to increase. Observing this phenomenon as a form of concern and seriousness in preventing similar actions, Yasarini (Ardhya Garini Foundation), known as Sekolah Angkasa Lanud Soewondo, collaborated with the Developer Company PT. Taman Malibu Indah and Writers, located in the Mustang Hall Jalan Komodor Muda Adi Sucipto Medan, held activities in the form of Parenting for parents of students and Lectures for Middle and High School students at Sekolah Angkasa Lanud Soewondo, by providing an understanding of character values related to God, by yourself, with each other and the environment. The aim of the activity are the parents and students to know the importance of having good and strong character which can lead students had to become human resources advantage in welcoming Indonesia Gold in 2045.
A Hybrid GDHS and GBDT Approach for Handling Multi-Class Imbalanced Data Classification Hartono, Hartono; Zuhanda, Muhammad Khahfi; Syah, Rahmad; Rahman, Sayuti; Ongko, Erianto
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.894

Abstract

Multiclass imbalanced classification remains a significant challenge in machine learning, particularly when datasets exhibit high Imbalance Ratios (IR) and overlapping feature distributions. Traditional classifiers often fail to accurately represent minority classes, leading to biased models and suboptimal performance. This study proposes a hybrid approach combining Generalization potential and learning Difficulty-based Hybrid Sampling (GDHS) as a preprocessing technique with Gradient Boosting Decision Tree (GBDT) as the classifier. GDHS enhances minority class representation through intelligent oversampling while cleaning majority classes to reduce noise and class overlap. GBDT is then applied to the resampled dataset, leveraging its adaptive learning capabilities. The performance of the proposed GDHS+GBDT model was evaluated across six benchmark datasets with varying IR levels, using metrics such as Matthews Correlation Coefficient (MCC), Precision, Recall, and F-Value. Results show that GDHS+GBDT consistently outperforms other methods, including SMOTE+XGBoost, CatBoost, and Select-SMOTE+LightGBM, particularly on high-IR datasets like Red Wine Quality (IR = 68.10) and Page-Blocks (IR = 188.72). The method improves classification performance, especially in detecting minority classes, while maintaining high accuracy.
An Effective Hybrid Approach for Predicting and Optimizing Business Complexity Metrics and Data Insights Syah, Rahmad B.Y; Elveny, Marischa; Ananda, Rana Fathinah; Nasution, Mahyuddin K.M; Hartono, Hartono
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.830

Abstract

This study proposes a hybrid approach for optimizing complexity prediction in the domain of business intelligence by integrating three powerful techniques: the Multi-Objective Complexity Prediction Model (MPK), Principal Component Analysis (PCA), and the XGBoost regression algorithm. The MPK model serves as a state-based simulator to capture system complexity dynamics, while PCA is employed to reduce data dimensionality and eliminate redundancy among features. Subsequently, XGBoost is used as a non-linear predictive model to estimate complexity values based on the refined input features. The results show that this hybrid approach significantly improves prediction accuracy, reduces data noise, and streamlines the modelling process. Quantitative evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the R-squared (R²) metric demonstrates exceptional performance, with an MAE of 0.000035, an MSE of 6.7 × 10⁻⁹, and an R² of 0.9999999. These results confirm that the integration of MPK, PCA, and XGBoost is highly effective for complexity prediction tasks and can provide accurate and insightful outcomes in business intelligence analytics.
IMPROVING CYBERSECURITY TRAFFIC ANALYSIS VIA ENHANCED K-MEANS CLUSTERING WITH TRIANGLE INEQUALITY-BASED INITIALIZATION Hartono, Hartono; Khahfi Zuhanda, Muhammad; Rahman, Sayuti
Jurnal TIMES Vol 14 No 1 (2025): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51351/jtm.14.1.2025823

Abstract

Clustering algorithms are essential in data mining and pattern recognition for grouping unlabeled data into meaningful clusters based on similarities. Among them, K-Means is widely used due to its simplicity and efficiency but suffers from sensitivity to initial centroid selection and inability to capture feature dependencies. This study proposes an Enhanced Mutual Information-based K-Means (MIK-Means) algorithm combined with Triangle Inequality and Lower Bound (TILB) seeding to improve clustering accuracy and computational efficiency, particularly in the context of network traffic classification for cybersecurity applications. The TILB method accelerates the initialization phase by reducing redundant distance calculations using mathematical pruning techniques, thereby selecting well-distributed initial centroids efficiently. Meanwhile, MIK-Means incorporates mutual information as a similarity measure during clustering assignment, enabling the algorithm to capture complex statistical dependencies among features, which traditional Euclidean distance metrics fail to address. The combination of these two approaches results in a robust clustering framework capable of handling large-scale, high-dimensional, and noisy datasets commonly found in network intrusion detection. The proposed method was evaluated on several benchmark datasets including Darpa 1998-99, KDD Cup99, NSL-KDD, UNSW-NB15, and CAIDA. Comparative experiments with state-of-the-art algorithms such as K-Means++, K-NNDP, and DI-K-Means showed that the proposed approach consistently outperformed or matched competitors in terms of Silhouette Coefficient, Calinski-Harabasz index, and Davies-Bouldin index, indicating better cluster cohesion, separation, and compactness. Additionally, the computational efficiency gained from TILB seeding facilitates faster convergence without compromising clustering quality. Furthermore, a threshold-based cluster labeling mechanism was applied to translate clustering results into practical classifications for detecting attacks versus normal traffic, enhancing the usability of the method in real-world cybersecurity systems. Overall, this research demonstrates that the integration of TILB seeding and mutual information-based clustering provides an effective and efficient solution for network traffic classification challenges.
Pemanfaatan Limbah Organik untuk Pakan Ikan Berbasis Serangga BSF di Desa Marindal II: Utilization of Organic Waste using BSF Insect-Based Fish Feed in Marindal II Village Hartono, Hartono; Zuhanda, Muhammad Khahfi; Aramita, Finta; Suswati, Suswati; Rahman, Sayuti
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 8 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i8.9714

Abstract

This community service activity addresses two main issues in Marindal II Village, Patumbak Subdistrict, Deli Serdang Regency, North Sumatra Province: the high volume of organic waste and the need for fish feed production technology. The partner is a Women Farmers Group that manages chicken farming, goldfish and tilapia cultivation, and a banana plantation. Organic waste, particularly chicken manure, will be used as a medium for cultivating Black Soldier Fly (BSF) larvae, which produce maggots as fish feed. In addition to chicken manure, other waste such as vegetables, fruits, and kitchen scraps are also utilized. However, maggots alone are insufficient to meet the fish's nutritional needs, so an additional feed composition in pellets is required. Pellets are essential to prevent fish from being selective in their diet, thus ensuring their dietary needs are met. The community service team conducted awareness activities on waste utilization and nutritious pellet production for the partner and the community to promote the use of waste and prevent environmental pollution.
Co-Authors Aditya Pratama, Bayu Ammar Yasir Nasution Andi Rahmadsyah Andik Bintoro Andre Hasudungan Lubis Andriasan Sudarso Arnes Sembiring Asmah Indrawati B. Herawan Hayadi B. Herawan Hayadi Brilliant Handyman Manalu Citra Rahmadhani Cut Ita Erliana Dadan Ramdan Dahlan Abdullah Dedi Sahputra Desniarti Dian Maya Sari Elvie Maria Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erna Budhiarti Nababan Faadhil, Faadhil Finta Aramita Firman Syahputra Firman Syahputra Gio, Prana Ugiana Habib Satria Imelda Maelani Iqbal Giffari Ritonga Irwan Daniel Jaka Kusuma Jaka Kusuma Khairul Fadhli Margolang Limas, Agus Fahmi Maricha Elveny Marischa Elveny, Marischa Martini, Dewi Meli Handayani Mendarissan Aritonang Muhammad Ikhwani Muhammad Khahfi Zuhanda Muhammad Sadikin Muhammad Zarlis Muhammad Zulkarnain Lubis Mulkan Andika Situmorang N. Nazaruddin Nadapdap, Kristanty M. N. Nadra Ideyani Vita Nasution, Mahyuddin K.M Nos Sutrisno Nur Anzelina Nur Azelina Harahap Nursie, Aly Nurwijayanti Opim Salim Sitompul Prana Ugi Rachmat Aulia, Rachmat Rahmad B.Y Syah Rahmad Syah, Rahmad Rahman, Sayuti Rana Fathinah Ananda Retna Astuti Kuswardani Rezzy Eko Caraka Ria Wuri Andary Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly, Rika Rohima Rohima Rubianto Sabina Krisdayanti Samsul A Rahman Sidik Hasibuan Sembiring, Arnes Silvia Lestari Silvia Lestari Siti Aisyah Sitorus, Peniel Sam Putra Sugeng Riyadi suswati suswati Suswati Suswati Syah, Rahmad B.Y Tulus Tulus Wanayumini Wulan Dari Yeni Risyani Yudi Gebri Foenna Zakarias Situmorang