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All Journal Bulletin of Electrical Engineering and Informatics Journal of Nonformal Education Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Prosiding Seminar Nasional Ilmu Teknik
Jasmir, Jasmir
Teknik Informatika
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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PATTERN CLASSIFICATION SIGN LANGUAGE USING FEATURES DESCRIPTORS AND MACHINE LEARNING Nurhadi, Nurhadi; Winanto, Eko Arip; Said, Rahaini Mohd; Jasmir, Jasmir; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1228

Abstract

Sign language is way of communication for the deaf and speech impaired. In Indonesia, the utilization of a standardized language involves the incorporation of American Sign Language (ASL). ASL is employed for various communication needs, ranging from basic alphanumeric fingerspelling (A-Z and numbers) to the more complex SIBI form (comprising gesture vocabulary) in everyday interactions as well as formal contexts. This surge in the digitization of sign language underscores the ongoing advancements in research and development. The challenge in this research lies in the ability to recognize American Sign Language (ASL) with diverse intensities and invariant backgrounds. Therefore, the study emphasis is on proposing a suitable segmentation method comparison for multi-intensity ASL cases. Subsequently, global feature descriptor methods, including Color Histogram, Hu Moments, and Haralick Texture techniques, are applied for feature extraction. The result of the Logistic Regression method versus the supervised Random Forest checks accuracy and suitability in identifying ASL fingerspelling. The findings of this research is predictive value of logistic regression is 48%, with class Y having the highest precision (0.86), class V having the lowest accuracy (0.16), and class L having the highest recall (0.73). The maximum precision in classes B, F, H, I, K, Y, and Z is 1.00, and the lowest in class U is 0.58, while the highest recall is in class G, which is 1.00. The lowest is in class V, while the predictive value from the random forest is 86 percent. Class H has the greatest f1 score (0.99), while class U has the lowest f1 score (0.64). The Random Forest method outperforms the two methods suggested in the paper, according to the comparison.
Comparison and Data Visualization in Thyroid Cancer Disease Prediction Using Machine Learning Algorithms Yudha, M. Zahran; Jasmir, Jasmir; Fachruddin, Fachruddin
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2249

Abstract

Thyroid cancer is a common endocrine malignancy requiring accurate early prediction for improved patient outcomes. Comprehensive comparative studies of machine learning algorithms, accompanied by systematic visualization, remain limited. This study compares tree-based algorithms (Decision Trees, Random Forest) and boosting algorithms (Gradient Boosting, XGBoost) for thyroid cancer prediction and develops visualization strategies for clinical interpretation. Four algorithms were evaluated using accuracy (correct prediction proportion), precision (positive predictive value), recall (true positive rate), F1-score (harmonic mean of precision and recall), and AUC-ROC (area under the ROC curve). Visualization techniques, including confusion matrices, ROC curves, and feature importance plots, facilitated the interpretation of the model. XGBoost achieved superior performance with accuracy 95.2%, precision 94.8%, recall 95.6%, F1-score 95.2%, and AUC-ROC 0.978, followed by Random Forest (93.5%, 92.7%, 94.1%, 93.4%, 0.965), Gradient Boosting (91.8%, 90.9%, 92.4%, 91.6%, 0.952), and Decision Trees (87.3%, 86.5%, 88.2%, 87.3%, 0.913). Feature importance analysis identified key predictors. Boosting algorithms, particularly XGBoost, demonstrate superior thyroid cancer prediction across all metrics. Integrated visualization enhances clinical interpretability, providing empirical guidance for implementing machine learning-based diagnostic support systems.
Optimasi XGBoost Dengan SHAP Untuk Sistem Skrining Penyakit Jantung Clara Zuliani Syahputri; Jasmir Jasmir; Fachruddin Fachruddin
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.147

Abstract

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.
Perancangan Alat Deteksi Tingkat Kematangan Buah Mangga Indramayu Berdasarkan Kandungan Gas dan Pengolahan Citra Menggunakan YOLOv11 Adi Kusuma; Jasmir Jasmir; Willy Riyadi; Ahmad Ahmad
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.151

Abstract

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.
Evolusi Performa Arsitektur Deep Learning melalui Optimasi Bertahap dan Interpretabilitas Grad-CAM untuk Klasifikasi Penyakit Ikan Air Tawar Sasa Kirana Wulandari; Fachruddin Fachruddin; Jasmir Jasmir
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.179

Abstract

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.
Fitur Information Gain untuk Meningkatkan Nilai Performa Pengklasifikasi Machine Learning pada Analisis Sentimen Komentar Spam Pengguna Youtube Jasmir, Jasmir; Gunardi, Gunardi; Rohaini, Eni; Naibaho, Ronald; Sukoco, Bambang; Jasmir , Jasmir
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Perkembangan pesat media sosial telah memberikan ruang bagi setiap individu untuk menyampaikan pendapat, baik berupa komentar positif maupun negatif terhadap konten yang mereka akses. Kemudahan dalam memberikan opini secara daring ini berdampak pada semakin besarnya jumlah ulasan yang tersedia. Namun, volume ulasan yang sangat besar sering kali sulit untuk dianalisis secara manual dan berpotensi menimbulkan bias dalam penilaian. Untuk mengatasi permasalahan tersebut, diperlukan pendekatan otomatis melalui klasifikasi sentimen yang bertujuan mengelompokkan opini pengguna ke dalam kategori positif atau negatif. Dalam penelitian ini digunakan tiga algoritma pembelajaran mesin, yaitu Naïve Bayes (NB), K-Nearest Neighbor (KNN), dan Random Forest (RF). Data penelitian diperoleh dari public dataset UCI Machine Learning. Fokus penelitian adalah meningkatkan kinerja klasifikasi dengan memanfaatkan teknik seleksi fitur information gain. Hasil eksperimen menunjukkan bahwa penerapan information gain secara konsisten meningkatkan performa semua algoritma yang diuji, baik pada metrik akurasi, presisi, recall, maupun f1-score. Naïve Bayes awalnya memperoleh akurasi tertinggi sebesar 74,33% pada kondisi tanpa fitur tambahan. Namun, setelah penerapan information gain, algoritma KNN menunjukkan hasil paling optimal dengan akurasi mencapai 81,28% serta performa yang relatif seimbang pada semua metrik evaluasi. Sementara itu, Random Forest juga mengalami peningkatan, meskipun tidak melampaui KNN. Secara keseluruhan, penelitian ini menegaskan bahwa pemilihan fitur yang relevan melalui information gain mampu meningkatkan efisiensi dan efektivitas klasifikasi sentimen, serta dapat menjadi pendekatan yang potensial untuk menganalisis opini dalam skala besar.   Abstract The rapid growth of social media has provided individuals with the opportunity to freely express their opinions, whether positive or negative, toward the content they encounter. The increasing ease of sharing opinions online has resulted in a massive volume of user reviews. However, the large number of reviews is difficult to analyze manually and may introduce bias in interpretation. To address this issue, sentiment classification is applied to automatically categorize user opinions into positive or negative classes. In this study, three machine learning algorithms were employed: Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). The dataset was obtained from the public UCI Machine Learning repository. The main objective of this research is to improve classification performance by utilizing feature selection through the information gain method. Experimental results demonstrate that applying information gain consistently enhances the performance of all evaluated algorithms across multiple metrics, including accuracy, precision, recall, and F1-score. Without feature selection, Naïve Bayes achieved the highest accuracy of 74.33%. However, after applying information gain, KNN outperformed the other algorithms by reaching an accuracy of 81.28% and exhibited balanced results across all evaluation metrics. Random Forest also showed improvement but did not surpass the performance of KNN. Overall, these findings highlight the importance of feature selection in improving both the efficiency and effectiveness of sentiment classification. Furthermore, the use of information gain proves to be a promising approach for large-scale opinion analysis, particularly in handling the high dimensionality of textual data.
Evaluasi Kinerja Machine Learning pada Klasifikasi Penyakit Jantung Menggunakan Teknik Penyeimbangan Data Eni Rohaini; Gunardi, Gunardi; Nurhayati Nurhayati; Jasmir Jasmir; Zahra Prisdian Tiararosa
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.59

Abstract

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.
Analisis Sentimen Ulasan Penggunaan Aplikasi Maxim Pada Google Play Store Menggunakan Algoritma Naive Bayes, SVM, CatBoost Berbasis NLP Nanda Mediya Sari; Jasmir Jasmir; Elvi Yanti
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.65

Abstract

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.
Implementasi Data Mining dengan Teknik Smote dan Fitur Gain Ratio Untuk Klasifikasi Kelayakan Siswa Penerima PIP di Kota Jambi Dea Sabrina Candra; Jasmir Jasmir; Yanti, Elvi
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.66

Abstract

The Indonesia Pintar Program (PIP) is an educational assistance program for students from underprivileged families, but determining the eligibility of recipients still faces obstacles in the form of subjectivity and data imbalance. This study aims to classify the eligibility of high school students receiving PIP in Jambi City using data mining methods. The SMOTE technique was applied to overcome class imbalance, and Gain Ratio feature selection was used to determine important attributes. The dataset used consisted of 19,596 student data with a training data distribution of 70% and testing data of 30%. The classification process used the Naïve Bayes, Decision Tree (J48), and Random Forest algorithms with the Use Training Set, 5-Fold, and 10-Fold Cross Validation testing schemes. The results show that SMOTE improves model performance, but feature selection in some cases reduces accuracy. Overall, Random Forest without feature selection provides the best results with an accuracy of 93.33% and is recommended as the most effective model for objectively determining PIP recipient eligibility.
Implementasi YOLOv8 dan Pengaruh Augmentasi Data dalam Sistem Deteksi Faktor Risiko Sudden Infant Death Syndrom (SIDS) pada Bayi Rhadis Steffani Saputri; Jasmir Jasmir; Gunardi Gunardi
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.69

Abstract

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.
Co-Authors Abdul Rahim Abidin, Dodo Zaenal Adi Kusuma Agus Siswanto Agus Siswanto Agustina Ahmad Ahmad Alya Rafina Andika Pratama Arvita, Yulia Ayu Anggelina Ayu, Nur Trisna Azmi, M. Fikri Bambang Sukoco Caterina Paras Dewi Clara Zuliani Syahputri Dea Sabrina Candra Denia Igesti Nur Mellyati Dodo Zaenal Abidin Elvi Yanti Fachruddin Fachruddin Fachruddin Fachruddin Faiza Rini Fatima Anggraini Fransiskus Dapot Sihaloho Fun, Chi-Win Gunardi Gunardi Hasanah Arif, Surya Hendri Hendri Heri Santoso Herti Yani, Herti Hidayatullah, Arief Irawan Irawan Jasmir , Jasmir Juliana Juliana Jumairi, Nopri Kurniabudi, Kurniabudi Lasmedi Afuan Lastari, Widya Mahruzar, Mahruzar Margaretta S, Chinthya Maria Rosario Borroek Marrylinteri Istoningtyas Meisak, Despita Meyer Mega Eklesia Silaban Muhammad Al-Aziz Sofyan Muhammad Arief Maulana Mulyadi Rusli NAIBAHO, RONALD Nanda Mediya Sari Nurhadi Nurhayati Nurhayati OSCARIO OSCARIO Osviarni, Rice Pareza Alam Jusia, Pareza Alam Parlindungan H, Edwardo Purnama, Benni Putri, Azhiah Raden Mohamad Herdian Bhakti Rasywir, Errisya Rawal Dewa, Suhendra Rhadis Steffani Saputri Rohaini, Eni Rosario , Maria Sadikin , Ali Said, Rahaini Mohd Sasa Kirana Wulandari Setiawan Assegaf Sharipuddin, Sharipuddin Sidik Praptomo Sophia, Aya Srikandi Alifya Suryawahyuni Latief Takhir, Said Hambali Trivenna, Priskha WIJAYA, IBNU SANI WILLY RIYADI Yan Apriadi Yanti, Elvi Yosefina Venus Yudha, M. Zahran Yudi Novianto Zahra Prisdian Tiararosa Zudyanti Dwi Rahma Sari