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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi 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 RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Eksplora Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa 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
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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Weighted nearest neighbors and radius oversampling for imbalanced data classification Pradipta, Gede Angga; Wulaning Ayu, Putu Desiana; Liandana, Made; Hostiadi, Dandy Pramana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp416-427

Abstract

The challenges associated with high-dimensional and imbalanced datasets were observed to often lead to a degradation in the performance of classical machine learning algorithms. In the case of high dimensional data, not all features contribute significantly and are considered relevant to the performance of the model. Therefore, this study introduced a novel method called feature weighted variance analysis-nearest neighbors (WFVANN) which was developed on the foundation of k-nearest neighbors (KNN). The process involved modifying the calculation of the Euclidean distance by fully considering the relevance and contribution levels of features based on their Fvalue. WFVANN at the algorithmic level processing and radius-synthetic minority oversampling technique (R-SMOTE) at the data level processing used as the oversampling method later became the proposed model to solve the aforementioned issues. Moreover, extensive experiments were conducted on two distinct types of data including the high-dimensional and imbalanced by comparing WFVANN with the state-of-art KNN-based and synthetic minority oversampling technique (SMOTE)-based methods. The results showed that the proposed method had the highest accuracy, precision, recall, and F1-measure values across the majority of test datasets and outperformed the other methods.
Pemanfaatan Digital Marketing dan Sistem Pengelolaan Stok pada Usaha Ternak Ayam Caru di Desa Serampingan Tabanan Made, Liandana; Gede, Angga Pradipta; Dandy Pramana Hostiadi; I Made Darma Susila; Yohanes Priyo Atmojo; Putu Desiana Wulaning Ayu; Artamerta, Aditya Naray; Intaran, Arya Ngurah
WIDYABHAKTI Jurnal Ilmiah Populer Vol. 7 No. 1 (2024): 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.v7i1.426

Abstract

Bali dengan budaya dan tradisi Hindu yang kuat, memiliki kebutuhan tinggi akan ayam caru untuk berbagai upacara keagamaan. Di Desa Serampingan, Kecamatan Kerambitan, Kabupaten Tabanan, UMKM setempat memanfaatkan peluang ini sebagai sumber mata pencaharian utama. Namun, usaha ayam caru ini menghadapi tantangan dalam hal pemasaran yang terbatas pada wilayah lokal dan pencatatan keuangan yang kurang rapi, mengakibatkan tidak akuratnya perhitungan pendapatan. Untuk mengatasi kendala tersebut, tim pengabdian masyarakat memberikan pelatihan penggunaan platform e-commerce serta pengembangan aplikasi web untuk pengelolaan stok dan alur kas. (1) pengantar kegiatan; (2) pelaksanaan pelatihan; (3) diskusi dan tanya jawab; (4) penyerahan materi pelatihan; (5) evaluasi; (6) penyerahan materi pelatihan; dan (6) evaluasi. Tujuan utama dari kegiatan ini adalah untuk meningkatkan cakupan pasar dari usaha mitra dan membantu memfasilitasi usaha mitra dalam melakukan manajemen stok, seperti: penjualan pakan, penjualan ayam caru, dan pelaporan keuangan. Melalui inisiatif ini, mitra usaha berhasil memanfaatkan Tokopedia guna memperluas pasar ayam caru dan mengadopsi sistem pencatatan stok berbasis web. Sistem ini membantu mencatat stok dan penjualan dengan lebih efisien, meningkatkan transparansi dan akurasi dalam pengelolaan usaha.
Optimization of XGBoost Algorithm Using Parameter Tunning in Retail Sales Prediction Wijaya, Hendra; Hostiadi, Dandy Pramana; Triandini, Evi
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.82214

Abstract

In retail companies, the owner needs sales analysis to make decisions in the company's business processes. Several previous studies have introduced forecasting techniques using regression analysis, and classification approaches that need optimization. This article proposes a new approach to sales prediction using XGBoost, which is optimized by comparing the best performance from three optimization methods: Random search, grid search, and Bayesian optimization. The aim is to obtain the best comparative analysis and increase prediction accuracy. The novelty of the proposed model is determining the best value for each optimization method using XGBoost. The results of the evaluation show that the best results were achieved by the grid search optimization technique in the XGBoost model with an increase in the evaluation value R^2 from 97.31 to 98.41. The results of the proposed model analysis can help retail business owners in accurate sales predictions to determine the development of business processes.
Usability and Performance Comparison: Implementation of Tibero and Oracle Databases in the Context of CAMS Software Development Komang Yuli Santika; Hostiadi, Dandy Pramana; Ayu, Putu Desiana Wulaning
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.82519

Abstract

In the world of software development, the role of database systems is very vital. Enterprise software, designed to handle the complex challenges of large organizations, requires reliable and efficient databases. Oracle, one of the top choices in the industry, stands out with its performance and flexibility. On the other hand, Tibero, a relational DBMS from TmaxSoft, offers the high performance, reliability and scalability required in business environments that require big data management. This research was conducted to analyze the technical side of the Oracle and Tibero databases in the context of the CAMS (Customer Asset Management System) application, with a focus on usability and performance aspects. This research uses the Performance Testing method to evaluate CPU, Memory, Storage resource usage and TPS (Transaction Per Second) of the two databases as well as the System Usability Scale (SUS) to measure user experience. The results provide information to software developers in selecting databases that suit business needs, while contributing to the development of the information technology industry
Analysis of Combination Machine Learning Classification with Feature Selection Technique for Lecturer Performance Analysis Model Srinadi, Ni Luh Putri; Antarajaya, I Nyoman Suraja; Widhyastuti, Luh Putu Wiwien; Hostiadi, Dandy Pramana; Rini, Erma Sulistyo; Chawaphan, Pharan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 2 (2025)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4356

Abstract

Machine learning-based classification techniques are widely utilized for accurate analysis in various fields. This study focuses on assessing lecturer performance in higher education to enhance teaching standards and produce high-quality learning outcomes. Previous studies have employed multiparameter approaches, such as statistical correlation analysis, but these methods fail to achieve optimal accuracy and precision due to limited alignment with data characteristics. This research proposes a lecturer performance measurement model by evaluating three machine learning algorithms: k-Nearest Neighbors (k-NN), Decision Tree, and Na¨pve Bayes. The model integrates three feature selection techniques to improve classification performance: ANOVA, Information Gain, and Chi-Square. The study aims to enhance classification accuracy and assess the impact of feature selection techniques on performance metrics. A significant contribution of this research is introducing a dynamic feature selection approach tailored to data characteristics, which improves classification model performance. The methodology comprises three main stages: data loading and measurement of relevant parameters; data preprocessing, including filtering, cleaning, transformation, normalization, and feature selection; and performance evaluation using a machine learning-based classification approach. Experimental results demonstrate that the Decision Tree algorithm combined with Chi-Square feature selection achieved an accuracy of 0.887, precision of 0.903, recall of 0.887, and F1-score of 0.884. The proposed modelprovides a reliable framework for evaluating lecturer performance and can be utilized to recognize and reward high-performing lecturers effectively.
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.
Sentiment Analysis for Hotel Reviews Using Snowball and VADER Rustamaji, Abdullah; Huizen, Roy Rudolf; Hostiadi, Dandy Pramana
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.82556

Abstract

In the hotel industry, the role of hotel guests is very influential in the development and sustainability of business. Therefore, hotels need to provide services that can satisfy guests. However, many hotels still do not have an analysis system for guest comments. Hotels still manually conduct analysis by discussing with operational leaders to determine whether incoming guest comments contain positive, negative, or neutral sentiments. Previous research introduced guest sentiment analysis but has yet to have optimal accuracy. This paper proposes sentiment analysis using a combination of VADER and Snowball stemmer algorithms, which are tested using real datasets. The goal is to get accurate sentiment analysis results. The experimental results show that the VADER method combined with SnowBall Stemmer has better accuracy than other sentiment analysis methods, with an accuracy of 96.21%. The sentiment analysis model can be used as a basis for decision-making for hotel business owners.
Performance Analysis of Prediction Methods on Tokyo Airbnb Data: A Comparative Study of Hyperparameter-Tuned XGBoost, ARIMA, and LSTM Nurfalah, Rizal Farhan Nabila; Hostiadi, Dandy Pramana; Triandini, Evi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30631

Abstract

The rapid growth of the digital economy has increased the importance of accurately predicting Airbnb property occupancy rates, especially in dynamic and competitive markets such as Tokyo, Japan. Property owners face significant challenges in forecasting occupancy rates due to seasonal patterns, non-linear trends, and complex temporal dependencies within the data. Addressing these challenges, this study investigates the performance of ARIMA, XGBoost, and LSTM models in predicting Airbnb occupancy rates in Tokyo. The dataset is collected from Airbnb listings and includes relevant features such as location, price, customer reviews, and historical occupancy rates. The models were optimized using Grid Search for ARIMA and Random Search for XGBoost and LSTM to identify the best hyperparameter configurations. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²), which are more appropriate for regression tasks. The results indicate that XGBoost achieves the highest R² (0.23), followed by LSTM (0.19) and ARIMA (0.03). However, the low R² values suggest that the models struggle to capture occupancy rate variations, indicating the potential influence of unmodeled external factors such as seasonality and policy changes. This study highlights the importance of hyperparameter tuning in improving prediction accuracy and contributes by providing an in-depth comparison of regression-based models for Airbnb occupancy forecasting.
Rancangan Infrastruktur E-Learning untuk Peningkatan Kualitas Pembelajaran di SMKN 1 Banjar, Kecamatan Banjar, Kabupaten Buleleng, Bali Yohanes Priyo Atmojo; Hostiadi, Dandy Pramana; Ni Luh Putri Srinadi; Muhammad Riza Hilmi; Tubagus Mahendra Kusuma; Susila, I Made Darma; Made Liandana; Pradipta, Gede Angga; Putu Desiana Wulaning Ayu
WIDYABHAKTI Jurnal Ilmiah Populer Vol. 7 No. 3 (2025): Juli
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.v7i3.868

Abstract

Pengembangan Learning Management System (LMS) berbasis e-learning di SMK N 1 Banjar bertujuan untuk meningkatkan kualitas pembelajaran dengan memanfaatkan teknologi digital. Kegiatan ini dilaksanakan melalui beberapa tahap, dimulai dengan identifikasi kebutuhan infrastruktur teknis dan non-teknis serta perencanaan pelatihan bagi guru dan siswa. LMS yang dikembangkan menggunakan platform Moodle dan dilengkapi dengan berbagai fitur seperti pengunggahan tugas, kuis daring, forum diskusi, serta pengelolaan pengguna. Sistem ini juga menggunakan domain khusus yang dimiliki SMK yang bertujuan untuk memastikan akses yang mudah dan sistem yang terintegrasi. Pada tahap implementasi, LMS dikonfigurasi untuk mencakup pengaturan kelas, mata pelajaran, kuis, dan modul-modul interaktif lainnya sesuai dengan kurikulum yang ada. Guru diberikan pelatihan untuk menyusun materi pembelajaran digital yang efektif, sementara siswa dibimbing untuk menggunakan LMS secara mandiri dalam proses pembelajaran. Tujuan dari pengembangan LMS ini adalah untuk meningkatkan literasi digital dan kemampuan siswa dalam memanfaatkan teknologi dalam pembelajaran. Hasil dari pelaksanan kegiatan pengabdian adalah adanya sistem pembelajaran dalam bentuk e-learning yang di implementasikan di SMK N 1 Banjar yang dapat digunakan secara efektif oleh guru dan siswa. Melalui penerapan e-learning yang dilakukan, ekosistem pembelajaran daring yang lebih modern, efektif, dan berkelanjutan di SMK N 1 Banjar telah terbentuk, serta dapat menjadi model yang dapat diadaptasi oleh sekolah-sekolah lain untuk meningkatkan kualitas pendidikan mereka.
Design and Evaluation of a Hybrid AES-ECC Model for Secure Server Communication using REST API Saputra, Made Wisnu Adhi; Huizen, Roy Rudolf; Hostiadi, Dandy Pramana
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4989

Abstract

Security in server-to-server communication is essential, especially in open networks vulnerable to data breaches and service disruptions. However, many existing solutions rely on a single cryptographic algorithm, limiting their ability to address diverse threats. This study aims to develop and evaluate a hybrid security model by combining the Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) to ensure confidentiality, integrity, and authenticity of transmitted data. An experimental approach is applied through direct implementation in server communication. The model uses AES for symmetric encryption, ECC for dynamic session key exchange, and JSON Web Token (JWT) reinforced by nonce, timestamp, and HMAC-SHA256 for authentication and integrity verification. Test results show the model detects payload modification, replay attacks, JWT manipulation, and passive interception, with processing time still within an acceptable range. Communication efficiency is maintained with negligible payload overhead. The novelty of this research lies in integrating hybrid encryption with stateless authentication and integrity validation into a unified architecture. This integration allows security elements to be delivered systematically via REST API, making the model easy to adopt in existing architectures. The results of this study contribute to the advancement of secure API-based communication frameworks in the field of informatics, providing a practical, adaptable, and scalable solution for protecting data in distributed information systems.
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 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 Octaviani, Sela Pande Wira Andika, Pande Putu Desiana Wulaning Ayu Rizky Adhitya Ridholloh, Rizky Adhitya Rosalia Hadi Roy Rudolf Huizen Rustamaji, Abdullah Saputra, Made Wisnu Adhi Shofwan Hanief Tangkawarow, Irene 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