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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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New Innovation: Predicting Anemia with the K-Medoids Method and Quantum Computing Using Manhattan Distance Hartama, Dedy; Putri, Adelia; Solikhun, Solikhun
JST (Jurnal Sains dan Teknologi) Vol. 13 No. 2 (2024): July
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v13i2.83457

Abstract

The low accuracy of anemia diagnosis with the classical K-Medoids method shows the need for alternative, more effective techniques in processing medical record data. This research aims to analyze the effectiveness of the quantum computing approach as a solution to develop an anemia diagnostic method by integrating the K-Medoids algorithm and Manhattan distance calculation. This research is an experimental study with a comparative design. The research subjects comprised anemia patient medical record data covering 5 attributes and 1 target, with 20 samples taken from the Kaggle.com platform. Data collection was conducted using data mining techniques, while the instrument used was computational modeling software. The data was analyzed using the accuracy comparison method between the classical and quantum computing-based K-Medoids methods. The analysis results show that the quantum computing-based K-Medoids method can achieve 80% accuracy, which is equivalent to the classical K-Medoids method, but with higher data processing efficiency. This research confirms that integrating quantum computing in the K-Medoids method can be an alternative in diagnosing anemia, offering the potential for broader application to more complex medical record data. The implication of this research is the creation of opportunities for innovation in quantum computing-based medical decision support systems that are more efficient.
Optimization of Accuracy Improvement through Modified ShuffleNet Architecture in Rice Classification Ahmad, Abdullah; Hartama, Dedy; Windarto, Agus Perdana; Wanto, Anjar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6411

Abstract

Accurate rice classification is essential to determine the quality and market value of rice. Traditional methods of rice classification are often time-consuming and error-prone, so a more efficient and accurate solution is needed. This study aims to optimize rice classification using Convolutional Neural Networks (CNN) combined with the ShuffleNet architecture, which offers high computational efficiency without sacrificing accuracy. The dataset used comes from Kaggle, containing 8750 rice grain images divided into five classes: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The uniqueness of this study is the application of ShuffleNet Proposed in rice classification, which provides improved performance compared to basic CNN models such as MobileNet, ShuffleNet, and RestNet. The results showed that the MobileNet model achieved 80% accuracy, RestNet 94%, and ShuffleNet achieved 100% accuracy with precision, recall, and F1 values also 100%. However, the ShuffleNet model experienced overfitting when tested with new data, resulting in an accuracy of only 20%. To overcome this, further optimization was carried out on the model. The results of statistical tests (paired t-test and Wilcoxon test) show significant differences between ShuffleNet Proposed and other models, which proves that the improvements applied to this model provide significant improvements. The implications of this study can improve the efficiency and accuracy of rice classification, which has the potential to improve the quality and market value of rice in the agricultural industry.
Sentiment Classification on Indodax Using Term Frequency, FastText, and Neural Attention Models Hartama, Dedy; Riski, Ginanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6871

Abstract

The rapid growth of mobile-based investment platforms such as Indodax has triggered a surge in user-generated reviews that reflect public perception and sentiment. This study aimed to develop and evaluate sentiment classification models that can accurately classify Indonesian user reviews on the Indodax app into negative, neutral, and positive sentiments. A dataset of 11,000 reviews was collected via web scraping from the Google Play Store. Reviews were preprocessed, labeled using a lexicon-based unsupervised method, and balanced using oversampling. Two models were built: a Bidirectional LSTM (BiLSTM) with attention mechanism using FastText embeddings, and a Feedforward Neural Network (FFNN) using a hybrid feature vector combining TF-IDF and FastText. The evaluation was performed using accuracy, classification report, confusion matrix, and PCA visualization. The FFNN model outperformed the BiLSTM-Attention model with an accuracy of 97.07% compared to 96.00%. Both models demonstrated strong performance in classifying three sentiment classes, though the FFNN showed better separation in PCA space and higher macro-average metrics. This study demonstrates the effectiveness of combining statistical and semantic feature representations for sentiment classification in Indonesian text. The proposed approach is particularly valuable for low-resource languages and informal user-generated content.
Analisis Pola Minat Siswa Lulusan SMU/SMK Untuk Melanjutkan Kuliah dengan Menggunakan Algoritma C4.5 Ayunda, Yuli Septya; Hartama, Dedy; Lubis, Muhammad Ridwan; Gunawan, Indra; Rafai, Muhammad
TIN: Terapan Informatika Nusantara Vol 4 No 9 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i9.4880

Abstract

STIKOM Tunas Bangsa is one of the best private universities in the city of Pematangsiantar. With so many universities in the city of Pematangsiantar, it certainly creates competition in attracting prospective new students. In order to maintain the number of students each year, universities must know what factors are the main drivers for prospective students to continue their studies at STIKOM Tunas Bangsa. The aim of this research is to determine the main factors for high school/vocational school graduate students in continuing their studies at STIKOM Tunas Bangsa. Data was obtained from questionnaires which were then processed using data mining with the C4.5 algorithm and tested with Rapid Miner software. Based on the research results, it can be concluded that there are six (6) patterns produced in cases of interest in prospective students who will study at STIKOM Tunas Bangsa, where three (3) of these rules result in interest decisions and three (3) rules result in no interest decisions. One of the resulting rules is if Recommendation = Never, and Suitability of Department = Appropriate, then the result is Interest. Recommendations from local people are the main supporting factor for high school/vocational school graduate students to continue studying at STIKOM Tunas Bangsa Pematangsiantar.
IMPLEMENTATION OF DATA ANALYSIS HOTEL RATING LEVELS IN BALI USING THE K-MEANS ALGORITHM AND DECISION TREE Hamdani, Hamdani; Hartama, Dedy
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 3 (2024): Juni 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i3.2894

Abstract

Abstract: The service dramatically affects the number of guests staying at the hotel. Bali is the most visited tourist area by foreign tourists. Therefore, improved service is crucial for determining the rating level of a hotel. This research aims to combine two data mining algorithms: clustering and classification. This research is expected to contribute to hospitality in improving the best services for tourists, especially in the City of Bali.  Clustering algorithms are used to group the best number of hotels based on the four clusters selected from the k-means clustering algorithm. The classification algorithm using C4.5 determines the factors most dominant in determining the hotel rating level based on the gain ratio. The data used in this study results from observations on the website agoda.com in Bali of 51 data. The results of this study explained that cluster_0 is the highest-rated cluster, with a total number of 19 hotels found in claster_0. Data cluster0 is used for classification analysis using a decision tree, and the most dominant factor is the service factor, with an accuracy of 80%.            Keywords: data mining; kmeans; decision tree; hotel; bali;  Abstrak: Pelayanan sangat mempengaruhi jumlah pengunjung yang menginap dihotel. Bali merupakan daerah wisata paling banyak dikunjungi oleh wisatawan mancanegara. Oleh karena itu, peningkatan pelayanan sangat penting untuk penentuan level rating dari hotel. Tujuan dari penelitian ini untuk menggabungkan dua algoritma data mining yaitu clustering dan klasifikasi. Dengan penelitian ini diharapkan dapat memberikan kontribusi bagi perhotelan dalam meningkatkan pelayanan yang terbaik bagi wisatawan khususnya di Kota Bali.  Algoritma Clustering digunakan untuk mengelompokkan dari jumlah hotel yang terbaik berdasarkan empat cluster yang dipilih dari algoritma clustering berupa k-means. Algoritma klasifikasi menggunakan C4.5 digunakan untuk mengetahui faktor apa yang paling dominan dalam menentukan level rating hotel berdasarkan gain ratio. Data yang digunakan dalam penelitian ini hasil observasi di website agoda.com di bali sebanyak 51 data. Hasil dari penelitian ini menjelaskan dataset cluster_0 merupakan cluster rating tertinggi dengan jumlah 19 hotel yang terdapat di cluster_0. Data cluster_0 digunakan untuk analisis klasifikasi menggunakan decesion tree, didapat faktor yang paling dominan adalah faktor layanan dengan nilai akurasi sebesar 80%. Kata kunci: data mining; kmeans; decision tree; hotel; bali; 
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 : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

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;
Comparison of Hyperparameter Tuning Methods for Optimizing K-Nearest Neighbor Performance in Predicting Hypertension Risk Trianda, Dimas; Hartama, Dedy; Solikhun, Solikhun
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.v18i1.42260

Abstract

Hypertension is a major cause of cardiovascular disease, making early risk prediction essential. According to WHO, hypertension cases are estimated to reach 1.28 billion by 2023. This study aims to optimize the K-Nearest Neighbor (KNN) algorithm for predicting hypertension risk through hyperparameter tuning. Three methods Grid SearchCV, Bayes SearchCV, and Random SearchCV are compared to determine the best parameter configuration. The dataset, obtained from Kaggle, consists of 520 balanced samples (260 positive and 260 negative) with 18 health-related features such as age, gender, blood pressure, cholesterol, glucose, and others. After preprocessing, the KNN model is tuned using each method by testing combinations of neighbors (k), weight types, and distance metrics. Results show Bayes SearchCV achieved the highest accuracy of 92%, outperforming the baseline KNN model, which had 85% accuracy. The ROC AUC score of 0.96191 also indicates excellent classification performance. In conclusion, Bayes SearchCV significantly improves KNN's predictive ability in hypertension risk classification.
PENGUATAN KOMPETENSI DALAM PEMBELAJARAN PHP DAN MYSQL BAGI SISWA JURUSAN MULTIMEDIA SMK NEGERI 1 SIANTAR Damanik, Abdi Rahim; Hartama, Dedy; Saputra, Widodo; Tata Hardinata, Jaya
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 5, No 2 (2022): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v5i2.802-807

Abstract

Pengembangan ilmi serta teknologi sekarang semakin kedepan akan semakin maju karena sangat dibutuhkannya pengetahuan berbasis website. Pelatihan pembuatan website dengan PHP dan MySQL yang dilakukan pada SMK Negeri 1 Siantar dikarenakan pada SMK Negeri 1 tersebut dirasa belum maksimal dalam pengetahuan teknologi informasi yang telah diajarkan dikarenakan kondisi pandemi dan belajar daring, maka sebab itu pengabdian ini dilakukan. Adapun peserta dalam pengabdian ini adalah Siswa SMK Negeri 1 Siantar . Dalam kegiatan pengabdian ini  yaitu memberikan suatu pelatihan dan pendampingan untuk pembuatan Website dengan menggunakan PHP dan MySQL kepada para siswa dan siswi di SMK Negeri 1 Siantar. Adapun manfaat dari pelatihan ini adalah diharapkan dapat meningkatkan kemampuan para siswa-siswi SMK Negeri 1 Siantar dalam membuat website perpustakaan berbasis database mysql. Pentingnya pelatihan ini bagi SMK Negeri 1 Siantar karena dengan melihat perkembangan zaman tentang teknologi yang canggih oleh sebab itu siswa-siswi SMK Negeri 1 Santar juga dituntut untuk dapat menggunakan teknologi tersebut agar dalam proses ujian kompetensi keahlian nantinya mendapatkan nilai yang baik
OPTIMIZING SHUFFLENET WITH GRIDSEARCHCV FOR GEOSPATIAL DISASTER MAPPING IN INDONESIA Ahmad, Abdullah; Hartama, Dedy; Solikhun, Solikhun; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6747

Abstract

Accurate classification of natural disasters is crucial for timely response and effective mitigation. However, conventional approaches often suffer from inefficiency and limited reliability, highlighting the need for automated deep learning solutions. This study proposes an optimized Convolutional Neural Network (CNN) based on the lightweight ShuffleNet architecture, enhanced through GridSearchCV for systematic hyperparameter tuning. Using a geospatial dataset of 3,667 images representing earthquake, flood, and wind-related disasters in Indonesia, the optimized ShuffleNet model achieved a peak accuracy of 99.97%, outperforming baseline CNNs such as MobileNet, GoogleNet, ResNet, DenseNet, and standard ShuffleNet. While these results demonstrate the potential of combining lightweight architectures with automated optimization, the exceptionally high performance also indicates possible risks of overfitting and dataset bias due to limited variability. Therefore, future research should validate this approach using larger, multi-source datasets to ensure robustness and real-world applicability
QUANTUM-ASSISTED FEATURE SELECTION FOR IMPROVING PREDICTION MODEL ACCURACY ON LARGE AND IMBALANCED DATASETS Safii, Safii; Wahyudi, Mochamad; Hartama, Dedy
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7040

Abstract

One of the biggest obstacles to creating precise machine learning models is choosing representative and pertinent characteristics from big, unbalanced datasets. While too many features raise the risk of overfitting and computational expense, class imbalance frequently results in decreased accuracy and bias. The Simulated Annealing technique is used in this study to tackle a Quadratic Unconstrained Binary Optimization (QUBO) problem that is formulated as a quantum-assisted feature selection method to handle these problems. The technique seeks to reduce inter-feature redundancy and the number of selected features. There are 102,487 samples in the majority class and 11,239 in the minority class, totaling 28 characteristics in the experimental dataset. Nine ideal features were found during the feature selection method (12, 14, 15, 22, 23, 24, 25, 27, and 28). Ten-fold cross-validation was used to assess a Random Forest Classifier that was trained using an 80:20 split. With precision, recall, f1-score, and accuracy all hitting 1.00, the suggested QUBO+SMOTE method demonstrated exceptional performance. Comparatively, QUBO without SMOTE performed worse with accuracy 0.95 and minority-class f1-score of only 0.71, whereas a traditional Recursive Feature Elimination (RFE) approach obtained accuracy 0.97 with minority-class f1-score of 0.94. These findings indicate that QUBO can reduce dimensionality and address class imbalance which requires its integration with SMOTE. This study demonstrates how quantum computing can enhance the effectiveness and efficiency of machine learning, especially for large-scale imbalanced datasets
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