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All Journal TEKNIK INFORMATIKA Jurnal Sains dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JITK (Jurnal Ilmu Pengetahuan dan Komputer) Martabe : Jurnal Pengabdian Kepada Masyarakat KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JURTEKSI Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Computer Science and Information Technologies TIN: TERAPAN INFORMATIKA NUSANTARA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences BEES: Bulletin of Electrical and Electronics Engineering JOMLAI: Journal of Machine Learning and Artificial Intelligence Journal of Informatics, Electrical and Electronics Engineering Dike Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Hartama, Dedy
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Analisis Penerapan Data Mining Terhadap Kasus Positif Covid-19 Menggunakan Metode K-Means Clustering Azhari, Ridhan; Hartama, Dedy; Lubis, Muhammad Ridwan; Nasution, Della Fatricia; Windarto, Agus Perdana
Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 2 (2023): Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v3i2.1760

Abstract

This study has problems such as the absence of the use of the K-means clustering algorithm for data on positive COVID-19 cases in the Indonesian province. The purpose of this study is to apply the K-means clustering method in finding the closest distance to produce the lowest and highest clusters of data on positive COVID-19 cases in the Indonesian province. K-means is one of the algorithms in the non-hierarchical Clustering technique that tries to partition the existing data in the form of one or more clusters. The results obtained from the k-means clustering method produced 2 clusters, namely the lowest cluster C1 = 30 items while the highest cluster C2 = 4 items. This research can be used as a reference and can be further developed with other clustering methods or algorithms such as k-medoid in order to get a comparison of results and steps to use algorithms related to clustering.
PENERAPAN ALGORITMA K-MEANS UNTUK MENENTUKAN STATUS GIZI BALITA (STUDI KASUS: PUSKESMAS KECAMATAN JAWA MARAJA BAH JAMBI) Syaputri, Vera; Hartama, Dedy; Anggraini, Fitri; Safii, M.; Dewi, Rafiqa
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 6 No. 1 (2022): JATI Vol. 6 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v6i1.4630

Abstract

Gizi pada anak balita merupakan masalah yang sangat penting untuk diperhatikan terutama bagi orang tua dan tenaga kesehatan. Penelitian ini menggunakan teknik data mining yaitu algoritna K-Means untuk mengclustering gizi balita dengan menggunakan 3 cluster yaitu gizi baik, gizi buruk, dan obesitas. Variabel yang digunakan adalah berat badan dan tinggi badan balita. Berdasarkan hasil dari 60 data, jumlah balita yang mengalami status gizi baik pada puskesmas kecamatan jawa maraja pada cluster 0 terdapat 28 balita, pada cluster 1 terdapat 27 balita yang mengalami gizi buruk , dan terdapat 5 balita yang mengalami obesitas pada cluster 2. Diharapkan hasil penelitian ini dapat memberi masukan pada pihak puskesmas agar lebih memperhatikan asupan gizi pada balita sehingga dapat meningkatkan pertumbuhan dan perkembangan balita.
Perbandingan Algoritma Decision Tree, ID3, dan Random Forest dalam Klasifikasi Faktor-Faktor yang Mempengaruhi Karier Mahasiswa Ilmu Komputer Hartama, Dedy; Amalya, Nanda
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
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/jimik.v6i1.1113

Abstract

This study aims to compare the performance of three classification algorithms, namely Decision Tree, ID3, and Random Forest, in identifying factors that influence the careers of computer Science students. These algorithms are applied to a dataset that includes various student attributes, such as GPA, programming skills, and completed projects. The results show that Random Forest provides more accurate and stable prediction results than Decision Tree and ID3, especially in reducing the risk of overfitting. Students with high skills in Python and SQL and who focus on software development tend to choose a career in Software Engineering. While those involved in AI/ML-based projects tend to choose Data Science. The conclusions of this study provide valuable insights for educational institutions to design more effective career development strategies for students.
The Cumulative Capacitated Vehicle Routing Problem with Time-dependent on Humanitarian Logistics for Disaster Management Hartama, Dedy; Wanayumini, Wanayumini; Damanik, Irfan Sudahri
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.481

Abstract

This study addresses the challenges of optimizing humanitarian logistics during disaster management by developing a Cumulative Capacitated Vehicle Routing Problem with Time-Dependent factors (CCVRP-TD) model. The primary objective is to enhance delivery efficiency by incorporating time-dependent variables such as fluctuating traffic and service durations into route planning. The research contributes a novel Mixed Integer Nonlinear Programming (MINLP) framework that dynamically adapts to real-world conditions like road closures and shifting priorities. Using the MINLP approach, the model was validated through numerical experiments involving four delivery vehicles serving six customers across five routes. Results demonstrated a significant improvement in routing efficiency, with a total cumulative travel distance of 110 km and adherence to specified delivery windows, such as 9:30 AM and 10:30 AM for Customer 1. Additionally, vehicle capacity constraints were effectively managed, with individual route lengths ranging from 20 to 35 km. These findings showcase the model’s ability to balance cost minimization, service reliability, and logistical adaptability. The novelty lies in the integration of time-dependent costs and service benefits into a multi-depot framework, enabling flexible yet precise route optimization under constrained conditions. This research provides a robust tool for enhancing disaster logistics and offers practical implications for improving the responsiveness and effectiveness of humanitarian aid delivery.
Sistem Presensi Pegawai Berbasis Digital Signatures Dan Gps Location Damanik, Abdi Rahim; Hartama, Dedy; Sumarno, Sumarno; Gunawan, Indra
Dike Vol. 1 No. 1 (2023): Dike Edisi Februari
Publisher : CV. Ro Bema

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69688/dike.v1i1.11

Abstract

Digital signatures (Digital Signatures) is one of the authentication used by the user to add a verification that acts as a signature. In this study, the data collection process in the form of signatures by the user will be carried out to carry out employee attendance on every working day. The attendance process that is being carried out at STIKOM Tunas Bangsa still uses manual signatures which are very less effective in the data recording process. Technological advances both in terms of programming and web coding are one of the keys in the process of using digital signatures and a solution to facilitate employees in the employee attendance process. Digital signature documents are used to maintain the authenticity of the document as a whole. The authentication process is applied as a proof of the authenticity of digital-based signatures. If the digital-based signature is original, the digital-type file is still original and the owner is a legitimate person, with digitally signed and online verification that will make it easier both in terms of use and security of the employee attendance process. The application of GPS Location is useful for finding and storing location data for each employee when carrying out online attendance.
Penerapan Data Mining Algoritma C4.5 Terhadap Prediksi Faktor Menurunnya Hasil Panen Padi Siahaan, Nove Viktor Boyke; Poningsih, P; Suhendro, Dedi; Hartama, Dedy; Suhada, S
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 7, No 1 (2022): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v7i1.412

Abstract

The aim of the study was to predict the factors causing the decline in rice yields. By knowing the factors of declining rice yields, business owners can further evaluate the causes of declining rice yields and then look for solutions on how to overcome them. The method used in this study is the C4.5 Algorithm, the source of the data used is primary data obtained by direct interviews with rice mill owners and farmers in Siborna Village, Kec. Panei Kab. Simalungun Prov. North Sumatra. The variables used include (1) Pests, (2) Rice Grains, (3) Leaf Color, (4) Planting Month and (5) Planting Method. The results obtained 8 rules for the classification of factors causing the decline in rice yields with 3 increasing decision rules and 5 decreasing decision rules with an accuracy rate of 93.33%. It can be concluded that the predictor of the decline in rice yields is based on the connectedness of the Attributes of Planting Month, Pests, Rice Grains, Leaf Color and Planting Methods.
Model of emergence evacuation route planning with contra flow and zone scheduling in disaster evacuation Hartama, Dedy; Mawengkang, Herman; Zarlis, Muhammad; Widia Sembiring, Rahmad
Computer Science and Information Technologies Vol 2, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i1.p1-10

Abstract

Evacuation is characterized by rapid movement of people in unsafe locations or disaster sites to safer locations. The traffic management strategy for commonly used evacuations is the use of Shoulder-Lane, contra-flowing traffic and gradual evacuation. Contra-flow has been commonly used in traffic management by changing traffic lanes during peak hours. To implement the contra-flow operation, there are two main problems that must be decided, namely Optimal contra-flow lane configuration problem (OCLCP) and optimal contra-flow scheduling. Within the OCSP there are two approaches that can be used: zone scheduling and flow scheduling. Problem of contra-flow and zone scheduling problem is basically an Emergence evacuation route planning (EERP) issue. This research will discuss EERP with contra-flow and zone scheduling which can guarantee the movement of people in disaster area to safe area in emergency situation.
OPTIMIZATION OF K-MEANS AND K-MEDOIDS CLUSTERING USING DBI SILHOUETTE ELBOW ON STUDENT DATA Hartama, Dedy; Oktaviani, Selli
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 2 (2025): Maret 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3531

Abstract

Abstract: Clustering methods such as K-Means and K-Medoids are often used to analyze data, including student data, due to their efficiency. However, this method has weaknesses, such as sensitivity to selecting cluster centers (centroids) and cluster results that depend on medoid data. Clustering, an essential technique in data analysis, aims to reveal the natural structure of the data, even in the absence of labeled information. The study, conducted with complete objectivity, compared the performance of two popular clustering methods, K-Means, and K-Medoids, on student data. Three evaluation metrics, namely the Davies-Bouldin Index (DBI), silhouette score, and elbow method, were used to compare clustering and determine the ideal number of clusters for the two algorithms. The data taken in this study are in the form of names, attendance, assignments, formative, midterm exams, final exams, and quality numbers. Based on the existing optimization results, it can be concluded that the K-Means method excels in grouping Student Data. The best results were obtained from the K-Means Algorithm with the Silhouette Coefficient Method with a value of 0.7509 in cluster 2, and the Elbow Method with a value of 1428076.08 in cluster 2, DBI K-Medoids with a value of 0.7413 in cluster 3. So, the best cluster lies in 3 clusters.            Keywords: clustering; davies-bouldin indek; elbow method; k-means; k-medoids; silhouette score;  Abstrak : Metode clustering seperti K-Means dan K-Medoids sering digunakan untuk menganalisis data, termasuk data siswa, karena efisiensinya. Namun, metode ini memiliki kelemahan, seperti sensitivitas terhadap pemilihan pusat klaster (centroids) dan hasil klaster yang bergantung pada data medoid. Clustering, sebuah teknik penting dalam analisis data, bertujuan untuk mengungkapkan struktur alami dari data, bahkan tanpa adanya informasi berlabel.  Penelitian ini, yang dilakukan dengan objektivitas penuh, membandingkan kinerja dua metode clustering populer, yaitu K-Means dan K-Medoids, pada data mahasiswa. Tiga metrik evaluasi, yaitu Davies-Bouldin Index (D.B.I.), silhouette score, dan metode elbow, digunakan untuk membandingkan clustering dan menentukan jumlah cluster yang ideal untuk kedua algoritma tersebut. data yang diambil dalam penelitian ini berupa nama, kehadiran, tugas, formatif, ujian tengah semester, ujian akhir semester, angka mutu. Berdasarkan hasil optimasi yang ada, dapat disimpulkan bahwasannya metode K-Means unggul dalam pengelompokkan Data Mahasiswa. Sehingga di peroleh hasil terbaik dari Algoritma K-Means dengan Metode Silhouette Coefficient dengan nilai 0,7509 di cluster 2, dan Elbow Method dengan nilai 1428076,08 di cluster 2, DBI K-Medoids dengan nilai 0,7413 di cluster 3. Sehingga cluster terbaik terletak pada 3 cluster. Kata kunci: klasterisasi; davies-bouldin indek; elbow method; k-means; k-medoids; silhouette score;
Pelatihan Pemanfaatan Mendeley Desktop Sebagai Program Istimewa Untuk Akademisi Dalam Membuat Citasi Karya Ilmiah Windarto, Agus Perdana; Hartama, Dedy; Wanto, Anjar; Parlina, Iin
Aksiologiya: Jurnal Pengabdian Kepada Masyarakat Vol 2 No 2 (2018): Agustus
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/aks.v2i2.1319

Abstract

Desktop mendeley application is actually an application intended to facilitate the creation of citations and a list of libraries commonly used by the authors, so the authors will be pressed error in making the bibliography and facilitate in obtaining the writings to be cited. In addition to creating scientific papers, this application can also be used to manage the files of online journal articles that are the output of a scientific work. Furthermore, participants can utilize this application for the purpose of making a bibliography or collection of abstracts of certain fields of journal articles subscribed. Training activities undertaken in Community Service activities show that participants have a material understanding and the potential to make refernsi managers better and maximum by utilizing mendeley desktop applications.
A Hybrid GRG-Neighborhood Search Model for Dynamic Multi-Depot Vehicle Routing in Disaster Logistics Hartama, Dedy; Poningsih, Poningsih; Tanti, Lili
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.973

Abstract

In disaster relief logistics, timely and adaptive routing is critical to meet fluctuating demands and disrupted infrastructure. This paper proposes a Hybrid GRG–Neighbourhood Search (NS) model for solving the Multi-Depot Vehicle Routing Problem with Capacity and Time Dependency (MDVRP-CTD). The model integrates the Generalized Reduced Gradient (GRG) method for handling nonlinear capacity constraints and NS for local route refinement. The objective is to minimize total travel distance, delay penalties, and maximize vehicle utilization under dynamic disaster scenarios. Tested using the SVRPBench dataset, the hybrid model achieved up to 96.5% demand fulfillment, an 11% improvement in vehicle utilization, and a reduction in total distance by 7%, outperforming Tabu Search and ALNS in three simulation scenarios. The model demonstrates enhanced adaptability and responsiveness to time-sensitive, capacity-constrained environments. Its novelty lies in the integration of nonlinear optimization with adaptive local improvement tailored for disaster contexts, providing a robust decision-support tool for real-time humanitarian logistics.
Co-Authors , Rahmad Sembiring Abdillah, Mhd Khoiri Abdullah Ahmad Achmad Noerkhaerin Putra Agus Perdana Windarto Akbari, Imam Amalya, Nanda Ayunda, Yuli Septya Azhari, Ridhan B. Tambunan, Holpan Torang Damanik, Abdi Rahim Damanik, Bahrudi Efendi Dewi, Rafiqa Eko Purwanto Fitri Anggraini, Fitri Hamdani Hamdani Harefa, Darman Sahputra Herman Mawengkang Indra Gunawan Irfan Sudahri Damanik Jaya Tata Hardinata Kholis, Mu’Ammar Nur Lili Tanti MARIA BINTANG Mochamad Wahyudi Mochammad Imron Awalludin Muhammad Raqib Syahkur Muhammad Ridwan Lubis Muhammad Zarlis, Muhammad Nasution, Della Fatricia Nasution, Rizki Alfadillah Oktaviani, Selli P, Dini Rizky Sitorus Pangestu, Prayoga Parlina, Iin Poningsih Poningsih, Poningsih Poningsih, P Putra, Jaka Putri, Adelia Rafai, Muhammad Riski, Ginanti Sabila, Bunga Safii Safii Safii, M. Saragih, Irfan Christian Sari, Hanifah Urbach Sari, Juwita Permata Siahaan, Nove Viktor Boyke Solikhun Solikhun, Solikhun Suhada, S Suhendro, Dedi Sumarno . Syaputri, Vera Trianda, Dimas Wanayumini Wanto, Anjar Widodo Saputra Widyasuti, Meilin Winanjaya, Riki