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All Journal JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Tekno Kompak Jurnal Pengabdian Literasi Digital Indonesia
Dandy Pramana Hostiadi
Institut Teknologi dan Bisnis STIKOM Bali
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Social Sciences

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Pendampingan dan Implementasi Aplikasi Bank Sampah Pada TPS-3R Banjar Bonjaka I Made Darma Susila; Made Liandana; Dandy Pramana Hostiadi; Yohanes Priyo Atmojo; Gede Angga Pradipta; Putu Desiana WUlaning Ayu; Abdul Wahab; Muhamad Rihamzah
Jurnal Pengabdian Literasi Digital Indonesia Vol. 2 No. 1 (2023): June
Publisher : Puslitbang Akademi Relawan TIK Indonesia (ARTIKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/abdimas.v2i1.28

Abstract

Garbage is a common problem that needs serious attention. Waste problems can have an impact on public health and environmental health. The local governments and community's form of effort in dealing with waste problems is the formation of TPS-3R by applying the concept of reduce, reuse, and recycle. Implementing the TPS-3R Institution requires proper management with the support of information technology. One of the TPS-3Rs located in the Gianyar – Bali area is TPS-3R Banjar Bonjaka which provides waste processing services for 345 households. The problem encountered during the observation was the manual recording of TPS-3R operational activities. The recording is currently only in the form of a physical book which carries the risk of errors, loss of records, and physical damage. Thus, in carrying out community service activities, it is proposed to provide assistance activities and implement the Garbage Bank application for TPS-3R managers. Community service activities aim to increase the ability to use information technology to manage TPS-3R. Thus, it can help optimize administrative governance. The activity was carried out for one day and attended by 10-15 participants. The evaluation of community service activities shows that participants have been able to input or manage data in the waste bank application. Achievement of abilities is a benchmark for the success of community service activities, with a success percentage of around 80-90%. This activity's benefit is facilitating administrative recording and management of TPS-3R activities by implementing the waste bank application.
Pelatihan Video Content Creator pada Pelaku Jasa Pariwisata sebagai Sarana Pemasaran Digital kepada Wisatawan Ni Luh Putri Srinadi; Ida Bagus Suradarma; Dandy Pramana Hostiadi; Muhammad Riza Hilmi; Ni Nyoman Utami Januhari; Luh Putu Wiwien Widhyastuti
WIDYABHAKTI Jurnal Ilmiah Populer Vol. 5 No. 3 (2023): Juli
Publisher : STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/widyabhakti.v5i3.371

Abstract

Di masa pandemi, beberapa pelaku pariwisata mengalami penurunan ekonomi secara drastis dan tidak sedikit memilih untuk menutup usaha secara sementara. Kebangkitan ekonomi pariwisata di era baru pasca pandemi, dilakukan seperti pemasaran digital melalui pembuatan video promosi melalui konsep content creator. Sebatu Tulen merupakan salah satu pelaku pariwisata berupa usaha penyewaan vila di daerah Desa Sebatu, kecamatan Tegallalang, kabupaten Gianyar – Bali. Usaha ini sempat terpuruk di saat masa pandemi, dan saat ini mencoba untuk bangkit kembali. Beberapa hal yang menjadi fokus permasalahan adalah mengenalkan kembali layanan kamar ke wisatawan yang dapat dilihat secara visual dan menarik dalam bentuk video company profil. Dalam kegiatan pengabdian kepada masyarakat, diusulkan pelatihan pembuatan video profil dengan konsep video content creator kepada pada pegawai yang ada di Sebatu Tulen. Tujuannya adalah untuk menyajikan informasi secara visual yang naratif, komunikatif dan menarik, sehingga harapannya dapat menarik wisatawan untuk menginap. Hasil dari kegiatan pelatihan dan pembuatan video content creator adalah pengambilan 8-15 video durasi pendek. Hasil akhir video company profil adalah sebuah video profil kualitas HD 1920 x 1080 p, dengan keterbaharuan video profil yang informatif pada vila Sebatu Tulen sebagai sarana pemasaran digital di era baru pandemi sebagai representasi kebangkitan pariwisata bali.
Rancang Bangun Sistem Informasi Administrasi Kantor Pada Badan Usaha Milik Desa Studi Kasus Desa Pemecutan Kaja Mandiri Gede Angga Pradipta; Roy Rudolf Huizen; I Made Darma Susila; Dadang Hermawan; Putu Desiana Wulaning Ayu; Dandy Pramana Hostiadi
Jurnal Tekno Kompak Vol 17, No 2 (2023): Agustus
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v17i2.2332

Abstract

Badan Usaha Milik Desa (BUMDes) dikenal sebagai lembaga yang dikelola oleh masyarakat suatu desa. BUMDes umumnya memiliki banyak kegiatan yang bertujuan untuk menggerakan perekonominan masyarakat dalam suatu desa. Dalam perkembangannya, seringkali administrasi pencatatan kegiatan yang meliputi pencatatan aset, keanggotaan, akuntansi, arsip surat menyurat dan absensi pegawai belum dilakukan secara optimal. Hal ini menyebabkan beberapa teknik pencatatan administrasi menjadi tidak teratur, tidak sesuai, hilang serta pencatatan manual dalam buku sering kali hilang. Maka pencatatan secara komputerisasi atau dalam bentuk digitalisasi administrasi perlu dilakukan dalam kantor BUMDes untuk menertibkan mekanisme pencatatan yang bersifat administratif. BUMDes Pemecutan Kaja Mandiri merupakan salah satu BUMDes yang telah menerapkan pemanfaatan teknologi berbasis komputerisasi. Hal ini di dukung telah dilakukannnya pencatatan dalam dokumen digital, namun masih belum optimal. Pencatatan administrasi belum terkomputerisasi dalam bentuk sistem yang optimal dan bersifat partial. Sehingga memerlukan sebuah aplikasi terintegrasi dalam pemenuhan kebutuhan pencatatan administrasi di kantor BUMDes. Pada penelitian ini, diusulkan sebuah rancang bangun sistem informasi administrasi kantor yang bersifat dinamis dan interaktif berdasarkan kebutuhan pada BUMDes. Rancang bangun terdiri dari proses perancangan alur data, perancangan relasi antar entitas dalam sistem, implementasi dan pengujian sistem. Perancangan alur data dan relasi antar entitas menggunakan metode System Development Life Cycle (SDLC) dengan model waterfall yang terdiri atas analisis kebutuhan, perancangan desain, implementasi dalam bentuk kode pemrogram dan pengujian. Hasil dari rancang bangun adalah sebuah sistem informasi administrasi kantor BUMDes berbasis website. Terdapat 8 fitur utama dalam sistem informasi, yaitu pengelolaan biodata pengguna, dashboard sistem, data pegawai, profil kantor, pendataan surat masuk, pendataan surat keluar, inventaris dan absensi. Pengujian yang digunakan adalah blackbox testing dengan melihat fungsionalitas setiap fungsi dan tombol dalam laman website. Pengujian menunjukkan bahwa seluruh fungsionalitas sistem berjalan sesuai dengan yang diharapkan sengan kelas uji sebanyak 170. Berdasarkan pengujian telah diimplementasikan penginputan data, sistem dapat menampilkan engan baik dan sistem dinyatakan telah bekerja secara optimal.
Voting Scheme Nearest Neighbors by Difference Distance Metrics Measurement Gede Angga Pradipta; Made Liandana; Putu Desiana Wulaning Ayu; Dandy Pramana Hostiadi; Putu Sumardika Eka Putra
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.19298

Abstract

K-Nearest Neighbor (KNN) is a widely used method for both classification and regression cases. This algorithm, known for its simplicity and effectiveness, relies primarily on the Euclidean formula for distance metrics. Therefore, this study aimed to develop a voting model where observations were made using different distance calculation formulas. The nearest neighbors algorithm was divided based on differences in distance measurements, with each resulting model contributing a vote to determine the final class. Consequently, three methods were proposed, namely k-nearest neighbors (KNN), Local Mean-based KNN, and Distance-Weighted neighbor (DWKNN), with an inclusion of a voting scheme. The robustness of these models was tested using umbilical cord data characterized by imbalance and small dataset size. The results showed that the proposed voting model for nearest neighbors consistently improved performance by an average of 1-2% across accuracy, precision, recall, and F1 score when compared to the conventional non-voting method.
Performance Analysis of Deep Learning Architectures in Classifying Fake and Real Images Arya Faisal Akbar; Putu Desiana Wulaning Ayu; Dandy Pramana Hostiadi
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25790

Abstract

The advancements in artificial intelligence (AI) have significantly enhanced image manipulation capabilities, yet they also raise concerns regarding the proliferation of synthetic images. This study investigates the impact of Dynamic Dropout in optimizing deep learning models, including ResNet-101, DenseNet-201, VGG-19, and AlexNet, for classifying real and synthetic images using the CIFAKE and Real and Fake Face datasets. Dynamic Dropout was applied with a progressively increasing rate from 20 percent to 50 percent to enhance training stability and generalization. The results indicate that the optimal configuration consisting of 15 epochs, the Adam optimizer, and Dynamic Dropout consistently outperformed Static Dropout across all models. DenseNet-201 with Dynamic Dropout achieved the highest accuracy of 97.42%, with a precision of 97.33%, recall of 97.58%, and an F1-score of 97.45%. ResNet-101 and VGG-19 exhibited enhanced training stability, while AlexNet proved efficient for lightweight datasets. The Adam optimizer outperformed Nadam, offering greater stability in deeper architectures. Additionally, the 15th epoch was identified as the optimal training duration, balancing accuracy and overfitting mitigation. These findings underscore the importance of selecting optimal training configurations to enhance deep learning performance. Future research should explore adaptive dropout strategies, assess scalability on diverse datasets, and validate these techniques in real-world applications such as digital forensics and AI-generated content detection
HYBRID PSO K-MEANS AND ROBUST SPARSE K-MEANS FOR EMPLOYEE STUDY DECISIONS Luh Dwi Ari Sudawati; Roy Rudolf Huizen; Dandy Pramana Hostiadi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7101

Abstract

Human Resources (HR) are a strategic asset in institutional advancement, so employee performance evaluation must be conducted objectively and based on data. This study aims to cluster employee performance data at XYZ University for determining further studies, using the K-Means, PSO K-Means, and Robust Sparse K-Means methods, as well as three types of distance measurements: Euclidean, Manhattan, and Mahalanobis Distance. The dataset consists of 17 attributes. The evaluation was conducted using the Silhouette Score, Davies-Bouldin Index, and visualization using PCA. The results indicate that the combination of PSO K-Means with Euclidean Distance provides the best balance between quantitative performance (Silhouette Score 0.1253 and DBI 2.0521) and a more visually representative distribution of cluster members. The interpretation of the clustering results yielded three clusters: Cluster 0 (no further study) consisting of 8 employees, Cluster 1 (further study) consisting of 97 employees, and Cluster 2 (awaiting study decision) consisting of 58 employees. These findings can be utilized by institutions to design more targeted and data-driven human resource development strategies.
Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance Firgiawan Faira; Dandy Pramana Hostiadi; Roy Rudolf Huizen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6332

Abstract

Industry 4.0 requires secure networks as the advancements in IoT and AI exacerbate the challenges and vulnerabilities in data security. This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. The dataset initially showed a significant imbalance, with 2,934,447 entries of attack activity and only 370 entries of normal activity. To address this imbalance, an innovative data aggregation technique was applied, effectively reducing similar patterns and trends. This approach resulted in a balanced dataset consisting of 8 attack activity points and 80 normal activity points. Feature selection using the ANOVA method identified 10 key features from a total of 17: seq, stddev, N_IN_Conn_P_SrcIP, min, state_number, mean, N_IN_Conn_P_DstIP, drate, srate, and max. The classification process utilized Random Forest, k-NN, Naïve Bayes, and Decision Tree algorithms, with 100 iterations and an 80:20 training-testing split. Random Forest showed superior performance, achieving 97.5% accuracy, 97.4% precision, and 97.4% recall, with a total computation time of 11.54 seconds. Pearson correlation analysis revealed a strong positive correlation (+0.937) between N_IN_Conn_P_DstIP and seq, as well as a weak negative correlation (-0.224) between N_IN_Conn_P_SrcIP and state_number. The novelty of this research lies in the application of a data aggregation technique to address class imbalance, significantly improving machine learning model performance and optimizing training time. These findings contribute to the development of robust cybersecurity systems to effectively detect IoT-related threats.
Enhancing E-Commerce Churn Prediction Accuracy Through the Combination of Gradient Boosting and Feature Selection Yogi Prasetyo Hernoto; Dandy Pramana Hostiadi; Putu Desiana Wulaning Ayu
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7076

Abstract

As the number of e-commerce users continues to expand, analyzing user activity has become essential for improving service quality, since customers are a key factor in strengthening competitiveness and business performance in this industry. Churn, in which customers discontinue service due to dissatisfaction, requires accurate detection to support effective retention strategies. To improve churn prediction accuracy, this study explores the optimal combination of feature selection methods and machine learning classifiers. ANOVA F-Test, Information Gain, Chi-Square, Recursive Feature Elimination (RFE), Variance Threshold, and Least Absolute Shrinkage and Selection Operator (LASSO) are the six feature selection techniques that are assessed in this study. These are combined with three classification algorithms: Gradient Boosting (GB), Random Forest (RF), and Logistic Regression (LR). Gradient Boosting with either Information Gain or LASSO yields the best results, achieving approximately 98% accuracy, 97% precision, 92% recall, 96% F1-score, and 99% ROC-AUC, based on testing on a publicly available Kaggle dataset. This approach outperforms prior studies, providing a robust framework for e-commerce developers to anticipate customer loss. By identifying key predictors of churn, the proposed model offers actionable insights to enhance service and enable proactive customer management.
Optimization of Boosting-based Classification for Phishing Web Detection Using Ant Colony Optimization Kadek Adies Wiranegara; Dandy Pramana Hostiadi; Roy Rudolf Huizen
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 2 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i2.28480

Abstract

Background: Cybercriminals commonly use phishing attacks by manipulating domain and website characteristics to mislead users into revealing sensitive personal information. The increasing scale of phishing attacks demands automated detection mechanisms that are accurate, efficient, and reproducible. Objective: The purpose of this research is to compare and evaluate the performance of boosting-based machine learning models for phishing domain detection integrated with Ant Colony Optimization (ACO) for feature selection. This study also aims to analyze the impact of ACO-based feature selection on classification performance and feature efficiency under consistent experimental conditions. Methods: Experiments were conducted using a public phishing webpage dataset from Kaggle, comprising 11,430 samples and 87 numerical features extracted from URL structures and webpage characteristics. Two scenarios were evaluated: training models with all features and with a reduced feature subset selected by ACO. Performance was assessed using Accuracy, Precision, Recall, F1-score, and ROC–AUC under a fixed train–test split and predefined hyperparameters. Result: Experimental results showed that LightGBM without feature selection achieved the highest accuracy 97.24%. However, ACO reduced feature dimensionality and improved computational efficiency in some models, including faster execution and lower memory usage for LightGBM, while also slightly decreasing accuracy. These findings indicate that ACO effectiveness is model-dependent and involves a balance between predictive performance and computational efficiency. Conclusion: The results confirm that boosting-based models effectively detect phishing domains, while ACO showed model-dependent effects on feature efficiency and computational trade-offs. Future work should use diverse datasets and systematic hyperparameter optimization to improve generalizability and performance.
Co-Authors Abdul Wahab Arya Faisal Akbar Dadang Hermawan Firgiawan Faira Gede Angga Pradipta, Gede Angga I Made Darma Susila Ida Bagus Suradarma Kadek Adies Wiranegara Luh Dwi Ari Sudawati Luh Putu Wiwien Widhyastuti Made Liandana, Made Muhamad Rihamzah Muhammad Riza Hilmi Ni Luh Putri Srinadi Ni Nyoman Utami Januhari, Ni Nyoman Putu Desiana Wulaning Ayu Putu Desiana Wulaning Ayu Putu Sumardika Eka Putra Roy Rudolf Huizen Roy Rudolf Huizen Yogi Prasetyo Hernoto Yohanes Priyo Atmojo