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An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning Hanafi, Hanafi; Muhammad, Alva Hendi; Verawati, Ike; Hardi, Richki
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.990

In the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection

PERBANDINGAN KINERJA ALGORITMA NAIVE BAYES DAN C4.5 DALAM PREDIKSI PENYAKIT JANTUNG Sri Wulandari; Kusrini Kusrini; Hanafi Hanafi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i2.284

Information Technology is a data processing technology and is a variety of ways to produce high-quality information accurately and quickly, relevant to the needs of individuals and businesses. Strategic information about decision making. The development of information technology is one of the most important factors for the progress of time. There are several fields that are important for technological progress and affect the progress of the country, such as the education sector, the economic sector, the health sector, the government sector, and the socio-cultural sector. Basically, technology is developed to promote human work. Currently, technology is a great need for humanity. In fact, technology is used in all aspects of human life. Predicting heart disease accurately is essential to treat heart patients efficiently before a heart attack occurs. This goal can be achieved by using an optimal machine learning model with complete heart disease health data. Therefore, a comparison of the performance of the Naive Bayes algorithm and the C4.5 algorithm in predicting heart disease requires calculation so that the results obtained are more accurate. Before doing the calculation, it is necessary to check the feasibility of the data to be used, then the division of training and testing data. In the study, there were several scenarios for dividing training and testing data using a confusion matrix. This study resulted in a performance comparison of the Naïve Bayes and C4.5 algorithms in predicting heart disease, 6 experimental scenarios were carried out, each algorithm had 3 experiments with varying amounts of training data and testing data. The C4.5 algorithm performed 3 experimental scenarios, in the first experiment the Naïve Bayes algorithm, the first experiment 70:30 produced an accuracy of 83%. In the second experiment 80:20 produced an accuracy of 83%. In the third experiment 90:10 produced an accuracy of 85%. Then the C4.5 algorithm performed 3 experimental scenarios, in the first experiment 70:30 produced an accuracy of 98%. In the second experiment 80:20 produced an accuracy of 100% In the third experiment 90:10 produced an accuracy of 100%.

Hyperparameter Optimization of TF-IDF and SVM via Grid Search for Sentiment Analysis of Traveloka Customer Reviews Muhammad Bayu Kurniawan; Hanafi; Riki Hikmianto; Isnawati Muslihah
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 2 (2025): October 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Customer reviews on digital platforms are crucial for improving services and making business decisions. This study focuses on automated sentiment analysis for Traveloka, a leading Indonesian online travel application. We propose a systematic hyperparameter optimization of a combined TF-IDF and Support Vector Machine (SVM) pipeline. A dataset of 20,200 user reviews was collected from the Google Play Store. After preprocessing and a two-stage labeling process, the data was split using stratified sampling (70% training, 30% testing). We conducted a comprehensive Grid Search with stratified 5-fold cross-validation to jointly optimize TF-IDF n-gram ranges (unigram, bigram, trigram) and SVM hyperparameters across four kernel types (Linear, RBF, Polynomial, Sigmoid). The results show that the Polynomial kernel with trigram features (C=5, gamma=1, degree=5, coef0=10) performs best. It achieves a test accuracy of 87.10% and a macro F1-score of 86.9%. Error analysis revealed the model's high reliability in detecting negative feedback (precision: 90.4%) but also its difficulty with contrastive sentences and informal language. The minimal performance differences among top configurations suggest the task is robust to specific parameter choices. However, the model's bag-of-ngrams approach shows limitations in processing contrastive sentences and informal language. For future work, employing contextual embeddings (e.g., IndoBERT) and exploring alternative algorithms like Random Forest or Neural Networks could address these challenges. This research presents a thoroughly optimized traditional ML methodology that establishes a strong baseline for automated sentiment analysis of Indonesian user feedback.

Analisis Sentimen Ulasan Mobile Banking Bank Kalbar pada Google Play Store Menggunakan IndoBert Hasrul Rahman; Hanafi
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9779

User reviews on the Google Play Store can serve as an important data source for understanding public perceptions of mobile banking services. At the time this paper was written, the application’s average user review score was 3.2, indicating a poor rating. Therefore, the bank needs to further examine the factors that could improve this rating in order to minimize reputational risk, since most users who intend to install an application tend to check its rating first before deciding whether to install it. In general, a rating considered very good is at least 4.5. This study aims to analyze user review sentiment toward the Bank Kalbar Mobile Banking application using a natural language processing approach with IndoBERT, along with two comparison models: TF-IDF with Logistic Regression and RNN BiLSTM. The dataset consists of 2,465 reviews classified into three sentiment classes: positive, negative, and neutral. The data distribution shows class imbalance, with 1,445 positive reviews, 894 negative reviews, and 126 neutral reviews. The data were split using a stratified method into 70% training data and 30% testing data. The research stages included text cleaning, data splitting, model training, and evaluation using accuracy, macro-F1, precision, recall, and a confusion matrix. The experimental results show that the TF-IDF + Logistic Regression model achieved the best performance, with an accuracy of 0.8581 and a macro-F1 score of 0.6777. The RNN BiLSTM model obtained an accuracy of 0.8311 and a macro-F1 score of 0.6777, while IndoBERT achieved an accuracy of 0.8041 and a macro-F1 score of 0.6774. Although IndoBERT did not achieve the highest accuracy, it demonstrated better capability in identifying the neutral class, as indicated by a recall score of 0.6316. These findings indicate that, for a relatively small and imbalanced dataset, a classical TF-IDF-based model can still deliver competitive performance compared with deep learning and transformer-based models.

PERBANDINGAN KINERJA ALGORITMA NAIVE BAYES DAN C4.5 DALAM PREDIKSI PENYAKIT JANTUNG Sri Wulandari; Kusrini Kusrini; Hanafi Hanafi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i2.284

Information Technology is a data processing technology and is a variety of ways to produce high-quality information accurately and quickly, relevant to the needs of individuals and businesses. Strategic information about decision making. The development of information technology is one of the most important factors for the progress of time. There are several fields that are important for technological progress and affect the progress of the country, such as the education sector, the economic sector, the health sector, the government sector, and the socio-cultural sector. Basically, technology is developed to promote human work. Currently, technology is a great need for humanity. In fact, technology is used in all aspects of human life. Predicting heart disease accurately is essential to treat heart patients efficiently before a heart attack occurs. This goal can be achieved by using an optimal machine learning model with complete heart disease health data. Therefore, a comparison of the performance of the Naive Bayes algorithm and the C4.5 algorithm in predicting heart disease requires calculation so that the results obtained are more accurate. Before doing the calculation, it is necessary to check the feasibility of the data to be used, then the division of training and testing data. In the study, there were several scenarios for dividing training and testing data using a confusion matrix. This study resulted in a performance comparison of the Naïve Bayes and C4.5 algorithms in predicting heart disease, 6 experimental scenarios were carried out, each algorithm had 3 experiments with varying amounts of training data and testing data. The C4.5 algorithm performed 3 experimental scenarios, in the first experiment the Naïve Bayes algorithm, the first experiment 70:30 produced an accuracy of 83%. In the second experiment 80:20 produced an accuracy of 83%. In the third experiment 90:10 produced an accuracy of 85%. Then the C4.5 algorithm performed 3 experimental scenarios, in the first experiment 70:30 produced an accuracy of 98%. In the second experiment 80:20 produced an accuracy of 100% In the third experiment 90:10 produced an accuracy of 100%.

PERBANDINGAN KINERJA PRE-TRAINED LANGUAGE MODEL BAHASA INDONESIA BERBASIS TRANSFORMER UNTUK ANALISIS SENTIMEN ULASAN TOKOPEDIA GOOGLE PLAY Muhammad Kevin; Hanafi Hanafi
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 3 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/91g0jx56

Analisis sentimen terhadap ulasan aplikasi e-commerce pada Google Play Store merupakan salah satu pendekatan penting untuk memahami persepsi pengguna. Namun, karakteristik data yang tidak terstruktur, penggunaan bahasa informal, singkatan, serta variasi dialek dan bahasa daerah masih menjadi tantangan dalam proses klasifikasi sentimen. Penelitian ini membandingkan performa tiga Pre-trained Language Model (PLM) berbahasa Indonesia, yaitu BERT Indonesia, RoBERTa Indonesia, dan IndoBERT, dalam mengklasifikasikan sentimen ulasan Tokopedia berbahasa Indonesia. Sebagai pembanding terhadap aspek efisiensi komputasi, DistilBERT Indonesia turut dievaluasi untuk menganalisis trade-off antara performa klasifikasi dan kebutuhan sumber daya komputasi. Dataset penelitian terdiri atas 10.000 ulasan Tokopedia yang diperoleh melalui web scraping menggunakan google_play_scraper. Tahapan penelitian meliputi preprocessing (pembersihan data, normalisasi teks, dan tokenisasi subword), pelabelan sentimen (positif, negatif, dan netral), pembagian data, proses fine-tuning menggunakan pustaka Hugging Face Transformers, serta evaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa IndoBERT memperoleh performa terbaik dengan akurasi pengujian sebesar 92,65%, diikuti oleh BERT Indonesia (92,15%) dan RoBERTa Indonesia (91,78%), yang menunjukkan bahwa ketiga model menghasilkan performa klasifikasi yang relatif kompetitif dan selisih akurasi yang kecil. Meskipun memiliki akurasi yang sedikit lebih rendah, DistilBERT Indonesia mencapai akurasi 91,78% dengan waktu pelatihan tercepat, sehingga menawarkan efisiensi komputasi yang lebih baik dibandingkan model lainnya. Temuan ini menunjukkan bahwa pemilihan pretrained language model tidak hanya dipengaruhi oleh tingkat akurasi, tetapi juga oleh karakteristik arsitektur dan efisiensi komputasi yang diperlukan pada proses analisis sentimen berbahasa Indonesia. Kata Kunci: Analisis Sentimen, Natural Language Processing, BERT Indonesia, IndoBERT, Tokopedia.

Optimasi Hyperparameter Optuna Pada Model mT5 Untuk Penerjemahan Angkola-Indonesia Awal Ridho Harahap; Hanafi Hanafi
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9465

This research aims to address the challenges of preserving the Angkola language in the digital era, which are exacerbated by the lack of an adequate digital data corpus, by developing an accurate and efficient automatic Angkola-to-Indonesian machine translation system. The proposed method focuses on a fine-tuning approach for the Multilingual Text-to-Text Transfer Transformer (mT5-base) model using an Angkola-Indonesian text data corpus.The initial dataset, consisting of Angkola-Indonesian sentence pairs, was cleaned, resulting in 28,775 sentence pairs used for training. The data was subsequently split into 70% training data (20,142 lines), 15% validation data (4,316 lines), and 15% test data (4,317 lines). Intelligent model performance optimization was conducted using Optuna Hyperparameter Tuning to find the best hyperparameter combination. Optuna's objective function was designed to maximize a composite score based on the BLEU and chrF metrics from the validation evaluation results. The optimization process yielded the best Trial (Trial 50) with key hyperparameters: learning rate = 0.0004316 and num beams = 4. The best model obtained from the fine-tuning process was then evaluated on a separate Test dataset. The final evaluation on the test data using standard translation metrics demonstrated excellent performance, achieving a BLEU score of 73.84 and a chrF score of 83.34. Overall, this research successfully implemented hyperparameter optimization using Optuna for the mT5 model, resulting in an Angkola-to-Indonesian translation model that exhibits high accuracy and more efficient performance. These results provide a tangible contribution to the preservation of the Angkola language by offering a modern and accurate translation tool.

The effectiveness of using RFID and IoT in digital transformation processes in garment companies using the UTAUT model2 Thedjo Sentoso; Kusrini Kusrini; Hanafi Hanafi
Gema Wiralodra Vol. 14 No. 2 (2023): gema wiralodra
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/gw.v14i2.511

This study aims to analyze the effectiveness of using RFID and IoT in the digital transformation process in a garment company using the UTAUT2 model. This research is necessary because it can influence the intentions and behavior of its users to increase production effectiveness. A quantitative approach uses the survey method used in this study to achieve the research objectives. The number of respondents in this study was 193 employees who worked in the preparation area. The data collected from the questionnaire results were analyzed using inferential statistics. The study results show that employee acceptance of using RFID and IoT in the digital transformation process gets a positive response. Each variable average value used is in the value range 3.79 – 4.44 (scale 1 to 5). In addition, it was found that Performance Expectancy, Effort Expectation, and Price Value positively influenced Behavioral Intention. In contrast, Habit and Behavioral Intention positively influenced Use Behavioral. As for the Social Influence and Hedonic Motivation variables on Behavioral Intentions and the Facilitating Conditions variable on Usage Behavior, no positive effect was found.

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