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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PROSIDING CSGTEIS 2013 Jurnal Sistem dan Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JOSINFO : Jurnal Online Sistem Informasi JOSIKOM : Jurnal Online Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF)
Dandy Pramana Hostiadi
STIKOM Bali
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Implementasi Alat Pengusir Burung Otomatis Berbasis Embedded System di Lahan Pertanian Gede Angga Pradipta; Made Liandana; Putu Desiana Wulaning Ayu; I Made Darma Susila; Dandy Pramana Hostiadi; Yohanes Priyo Atmojo
WIDYABHAKTI Jurnal Ilmiah Populer Vol. 8 No. 1 (2025): Nopember
Publisher : Direktorat Penelitian, Pengabdian Masyarakat, dan HKI Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/widyabhakti.v8i1.875

Abstract

Gangguan hama burung pada lahan pertanian, khususnya di area persawahan, menjadi salah satu faktor yang menyebabkan penurunan hasil panen. Penelitian ini bertujuan untuk mengembangkan sebuah alat otomatis berbasis sistem tertanam (embedded system) yang berfungsi sebagai pengusir burung di sawah. Alat ini bekerja dengan menarik tali lonceng secara otomatis, sehingga menghasilkan bunyi yang dapat mengusir burung secara efektif. Sistem ini terdiri dari beberapa komponen utama, yaitu panel surya sebagai sumber energi, motor penggerak, lengan penarik, dan mikrokontroler sebagai pusat kendali. Mikrokontroler diatur untuk menggerakkan lengan penarik dan mengatur waktu operasional secara otomatis. Hasil pengujian menunjukkan bahwa alat ini mampu beroperasi secara stabil dan efektif dalam mengusir burung, serta dapat bekerja secara mandiri dengan memanfaatkan energi terbarukan dari panel surya. Dengan demikian, alat ini diharapkan dapat menjadi solusi praktis dan berkelanjutan dalam mengatasi masalah hama burung di sektor pertanian.
Optimization Model for Fake Account Detection on Twitter (X) Social Media using Feature Engineering and Machine Learning Approaches Perimawati, Ni Nyoman Eny; Huizen, Roy Rudolf; Hostiadi, Dandy Pramana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1727

Abstract

Twitter (X) has become an important platform for community interaction, but this also creates serious challenges due to the proliferation of fake accounts that can harm users and undermine credibility. Previous studies have proposed detection methods but often lacked forensic analysis based on extracted feature information. This study utilizes labeled datasets and supervised evaluation metrics (precision, recall, and F1-score) to validate model performance. Extracting behavioral information from features is crucial for achieving accurate and reliable detection results. The study introduces a novelty in the form of engineered behavioral features that significantly enhance detection accuracy, achieving up to 99.94% using AdaBoost. The proposed approach detects fake accounts on Twitter (X) by extracting key feature information and developing an optimal detection method through machine learning algorithms, including Random Forest, SVM, and AdaBoost. Furthermore, the model is optimized using feature engineering techniques. The novelty of this work lies in the development of engineered features through distribution analysis based on data characteristics and the improvement of classification performance through feature engineering optimization. The initial experiment without feature engineering shows that Random Forest achieved the highest accuracy of 98.77%, followed by AdaBoost at 98.57% and SVM at 95.90%. After applying feature engineering, performance improved, with AdaBoost reaching 99.94%, Random Forest 99.69%, and SVM 99.32%. The proposed model can assist system analysts in detecting fake accounts and contribute to solving forensic cybercrime challenges, particularly in identifying fake social media profiles.
HYBRID PSO K-MEANS AND ROBUST SPARSE K-MEANS FOR EMPLOYEE STUDY DECISIONS Sudawati, Luh Dwi Ari; Huizen, Roy Rudolf; Hostiadi, Dandy Pramana
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 of Infected Salmon Using CNN Deep Features and Optuna-Optimized SVM Pradita, Agus Hendra; Ayu, Putu Desiana Wulaning; Hostiadi, Dandy Pramana
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.108820

Abstract

Fish diseases are a major challenge in the aquaculture industry, impacting productivity and the economy, particularly in salmon farming. This study aims to develop an image classification system for infected salmon using Convolution Neural Network (CNN) deep features approach and Support Vector Machine (SVM) classifier optimized with Optuna. The dataset consists of 1,208 images that were balanced through augmentation before being divided into 70% training data and 30% test data. Features were extracted from the middle layer of three pretrained CNN architectures: EfficientNetB1 (block6d_add), ResNet50 (conv4_block6_out), and VGG16 (block4_pool), then selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method to address high-dimensionality issues. An SVM classification model was trained using stratified 5-fold cross-validation, both with default parameters and hyperparameter optimization results from Optuna. The results show that the model with features from EfficientNetB1 tuned by Optuna achieved the highest accuracy of 99.34%, a significant improvement over the default model 98.23%. Meanwhile, ResNet50 and VGG16 achieved optimal accuracies of 98.23% and 98.89%, respectively, after tuning. This study contributes to the development of an adaptive and accurate early detection system for infected fish.
Co-Authors Amry wicaksono, Amry Anggreni Antarajaya, I Nyoman Suraja Artamerta, Aditya Naray Candra Ahmadi, Candra Chawaphan, Pharan Danang Setyo Utomo, Danang Setyo Dian Pramana S.Kom., M.Kom, Dian Erma Sulistyo Rini Erma Sulistyo Rini, Erma Sulistyo Eva Hariyanti Evi Triandini Fatonah, Nenden Siti Firgiawan Faira Florentina Tatrin Kurniati Gede Angga Pradipta Gede Angga Pradipta, Gede Angga Gede, Angga Pradipta Hendra Wijaya Hilmi, Muhammad Riza I G K G Puritan Wijaya. ADH, I G K G I Gede Harsemadi I Gede Ngurah Widya Pradnyana, I Gede Ngurah Widya I Gede Putu Krisna Juliharta I GKG Puritan Wijaya, I GKG I Gusti Ayu Dewi Suardi, I Gusti Ayu Dewi I Gusti Ngurah Darma Paramartha I Gusti Nym Adi Purnama Putra, I Gusti Nym I Made Darma Susila I Made Darma Susila, I Made I Made Darma Susila, I Made Darma I Made Liandana I Nyoman Triwantara Putra, I Nyoman I Putu Harry Wibawa Eka Putra, I Putu Harry Wibawa I Putu Oka Aditya Pratama I Putu Ramayasa, I Putu I Putu Widiantara, I Putu I Wayan Eka Mahardika, I Wayan Eka I Wayan Nesa Masjaya Perdana, I Wayan Nesa I.B. Putra Utama Dhiatmika, I.B. Putra Utama Ida Bagus Suradarma Indah, Hene Nor Intaran, Arya Ngurah Irene Realyta Halldy Trosi Tangkawarow Kadek Evanna Sidarta, Kadek Evanna Komang Yuli Santika Made Liandana Made Liandana, Made Made Sudarma Made, Liandana Mohammad Yazdi Pusadan Muhammad Riza Hilmi Ni Ketut Dewi Ari Jayanti Ni Luh Putri Srinadi Nurfalah, Rizal Farhan Nabila Pande Wira Andika, Pande Perimawati, Ni Nyoman Eny Pradita, Agus Hendra Putu Desiana Wulaning Ayu Rizky Adhitya Ridholloh, Rizky Adhitya Rosalia Hadi Roy Rudolf Huizen Rustamaji, Abdullah Saputra, Made Wisnu Adhi Shofwan Hanief Sudawati, Luh Dwi Ari Tubagus Mahendra Kusuma Widhyastuti, Luh Putu Wiwien Wulaning Ayu, Putu Desiana Wulaning Ayu, Putu Desiana Yohanes Priyo Atmojo Yohanes Priyo Atmojo Yohanes Priyo Atmojo, Yohanes Yohanes Priyo Atmojo, Yohanes Priyo Yudhi Pratiwindhya, Yudhi