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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Teknologi Informasi dan Ilmu Komputer ICON-CSE Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Sriwijaya Journal of Informatics and Applications Jurnal Teknik Informatika (JUTIF) Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Abdimas Maduma Media Elektrik
Primanita, Anggina
Universitas Sriwijaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Other

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A new system for underwater vehicle balancing control based on weightless neural network and fuzzy logic methods Zarkasi, Ahmad; Satria, Hadipurnawan; Primanita, Anggina; Abdurahman, Abdurahman; Afifah, Nurul; Sutarno, Sutarno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2870-2882

Abstract

The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.
Klasifikasi Kanker Payudara Menggunakan Metode Convolutional Neural Network (CNN) dengan Arsitektur VGG-16 Idawati, Idawati; Rini, Dian Palupi; Primanita, Anggina; Saputra, Tommy
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7553

Abstract

Breast cancer classification is a process to determine the type and characteristics of breast cancer based on the characteristics of cancer cells. In this research, a system is designed to classify breast cancer using ultrasound images which are then processed using the Convolutional Neural Network method with the VGG-16 architecture. The aim of the research is to develop a breast cancer classification system using Convolutional Neural Network (CNN) and evaluate the classification results using Convolutional Neural Network (CNN) with the VGG-16 architecture. In breast cancer classification, three classes are considered: normal, benign, and malignant. The steps in the classification process include image input, filtering, resizing, data augmentation, and data digitization. The best results were obtained in this test using the SGD optimizer hyperparameter, learning rate 0.001, epoch 20 and batch size 32 producing an accuracy value of 78.87%, a precision value of 75.69%, an AUC value of 79.85% and an f1 score value of 74.67%.
American Sign Language Translation to Display the Text (Subtitles) using a Convolutional Neural Network Ramadhan, Muhammad Fajar; Samsuryadi, Samsuryadi; Primanita, Anggina
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11904

Abstract

Sign language is a harmonious combination of hand gestures, postures, and facial expressions. One of the most used and also the most researched Sign Language is American Sign Language (ASL) because it is easier to implement and also more common to apply on a daily basic. More and more research related to American Sign Language aims to make it easier for the speech impaired to communicate with other normal people. Now, American Sign Language research is starting to refer to the vision of computers so that everyone in the world can easily understand American Sign Language through machine learning. Technology continues to develop sign language translation, especially American Sign Language using the Convolutional Neural Network. This study uses the Densenet201 and DenseNet201 PyTorch architectures to translate American Sign Language, then display the translation into written form on a monitor screen. There are 4 comparisons of data splits, namely 90:10, 80:20, 70:30, and 60:30. The results showed the best results on DenseNet201 PyTorch in the train-test dataset comparison of 70:30 with an accuracy of 0.99732, precision of 0.99737, recall (sensitivity) of 0.99732, specificity of 0.99990, F1-score of 0.99731, and error of 0.00268. The results of the translation of American Sign Language into written form were successfully carried out by performance evaluation using ROUGE-1 and ROUGE-L resulting in a precision of 0.14286, Recall (sensitivity) 0.14286, and F1-score.
Predictive Modeling of Air Quality Index Using Ensemble Learning and Multivariate Analysis Primanita, Anggina; Satria, Hadipurnawan
Sriwijaya Journal of Informatics and Applications Vol 5, No 2 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i2.121

Abstract

Breathing polluted air can result in multiple health problems. Thus, it is important to understand and predict the air quality in the environment. Air Quality Index (AQI) is a unit used to measure the air pollutants. In Indonesia, this value is measured and published by the Meteorological, Climatological, and Geophysical Agency regularly. In this research, four commonly used regression algorithms were used to analyzed AQI data, namely, Random Forest, Decision Tree, K-Neural Network, and Ada Boost. All the algorithms model were developed to analyzed 1096 AQI data. The Mean Squared Error value of each model was computed as a measure of comparison. It is found that the Random Forest is the best performing algorithm. It can generalize well without overfitting to the data set.
PERFORMANCE COMPARISON OF FACENET PYTORCH AND KERAS FACENET METHODS FOR MULTI FACE RECOGNITION Dedy Fitriady Fitriady; Samsuryadi Samsuryadi; Anggina Primanita
Jurnal Media Elektrik Vol. 22 No. 1 (2024): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v22i1.5899

Abstract

Face recognition has become an important technology in various applications, but challenges arise when multiple faces must be recognized simultaneously in a single image or video frame. This study develops a multi-face recognition system using the Multi-Task Cascaded Convolutional Neural Network (MTCNN) method for face detection, Pytorch Facenet and Keras Facenet for recognition, and Support Vector Machine (SVM) for classification. Using a dataset of 1000 images from 10 classes, this study compares the performance of Pytorch Facenet and Keras Facenet in terms of speed, memory usage, and accuracy. The results show that Pytorch Facenet is faster with an average of 0.15 seconds per image compared to Keras Facenet which requires 0.86 seconds per image, and is more efficient in memory usage with 384.19 MB lower. However, Pytorch Facenet uses 3% more CPU. In addition, in terms of accuracy, Pytorch Facenet shows a more stable and consistent confidence score. In conclusion, Pytorch Facenet proves to be more efficient and reliable for multi-face recognition, although it requires further CPU optimization for more optimal use in real application scenarios.
Adaptive Hint Generation for Educational Games Using Fuzzy Logic Anggina Primanita; Hadipurnawan Satria; Muhammad Qurhanul Rizqie; Ananda Haykel Iskandar; Wibisena Nugraha
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.41893

Abstract

The increasing interest in programming education has led to a wide variety of learner abilities. However, existing learning media often remain fragmented, necessitating the development of adaptive tools to cater to learners of varying skill levels. This study employs fuzzy logic to generate dynamic hints for players struggling to solve programming challenges in an educational game. The effectiveness of the system was evaluated through both simulation and real-world experiments. Simulation results indicate that the fuzzy logic system successfully generates personalized hints, with the highest frequency of hints provided to beginner players. Real-world testing using the GUESS-18 framework demonstrated high playability and excellent usability scores for the game.
Pemahaman Critical Thinking Dalam Menghadapi Olimpiade Sains Nasional (OSN) Untuk Guru SMA Al-Kautsar Bandar Lampung Rizki Kurniati; Osvari Arsalan; Anggina Primanita; Muhammad Fachrurrozi; Hadipurnawan Satria; Muhammad Qurhanul Rizqie; Ermatita
JURNAL ABDIMAS MADUMA Vol. 4 No. 2 (2025): Juli 2025
Publisher : English Lecturers and Teachers Association (ELTA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/jam.v4i2.434

Abstract

Berpikir kritis adalah kemampuan yang dibutuhkan oleh pengajar, terutama yang akan menghadapi Olimipade Sains Nasional (OSN). Tujuan dari kegiatan pengabdian kepada masyarakat ini adalah untuk meningkatkan kemampuan guru SMA dalam memahami permasalahan dengan berpikir kritis dalam menghadapi Olimpiade Sains Nasional (OSN). Melalui pelatihan dan pendampingan, para guru diberikan wawasan serta keterampilan praktis dalam mengintegrasikan metode berpikir kritis ke dalam pengajaran sehari-hari. Hasil kegiatan menunjukkan antusiasme tinggi dari peserta, yang terlihat dari interaksi aktif selama pelatihan. Kendala seperti keterbatasan infrastruktur jaringan diidentifikasi dan diusulkan solusi jangka panjangnya. Diharapkan dengan terselenggaranya kegiatan ini, kualitas pendidik dan pendidikan guru SMA mendapatkan dampak yang positif dan menjadi lebih baik. Hal ini dibuktikan dengan meningkatnya kemampuan guru SMA dalam memahmi soal berpikir kritis Kata Kunci : Berpikir Kritis; Gamifikasi; Olimpiade Sains Nasional; Pelatihan Guru; Pendidikan Digital
Machine Learning Models for Metabolic Syndrome Identification with Explainable AI Asoka, Egga; Fathoni, Fathoni; Primanita, Anggina; Isa, Indra Griha Tofik
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4430

Abstract

Metabolic syndrome (MetS) is a cluster of interrelated risk factors, including hypertension, dyslipidemia, central obesity, and insulin resistance, significantly increasing the likelihood of cardiovascular diseases and type 2 diabetes. Early identification of hypertension, a key component of MetS, is essential for timely intervention and effective disease management. This research aims to develop a hybrid machine learning model that integrates XGBoost classification with K-Means clustering to enhance or strengthening of hypertension prediction and identify distinct patient subgroups based on metabolic risk factors. The dataset consists of 1,878 patient records with metabolic parameters such as systolic and diastolic blood pressure, fasting glucose, cholesterol levels, and anthropometric measurements. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The proposed XGBoost model achieved an outstanding classification performance with 98% accuracy, 98% precision, 98% recall, 98% F1-score, and an ROC-AUC of 1.00. K-Means clustering further identified five distinct patient subgroups with varying metabolic risk profiles. The findings underscore the potential of machine learning-driven decision support systems in improving hypertension diagnosis and MetS management.
Analisa Perbandingan Algoritma A* dan Dynamic Pathfinding Algorithm dengan Dynamic Pathfinding Algorithm untuk NPC pada Car Racing Game Sazaki, Yoppy; Satria, Hadipurnawan; Primanita, Anggina; Syahroyni, Muhammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 1: Februari 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.231 KB) | DOI: 10.25126/jtiik.201851544

Abstract

Permainan mobil balap adalah salah satu permainan simulasi yang membutuhkan Non-Playable Character (NPC) sebagai pilihan lawan bermain ketika pemain ingin bermain sendiri. Dalam permainan mobil balap, NPC membutuhkan pathfinding untuk bisa berjalan di lintasan dan menghindari hambatan untuk mencapai garis finish. Metode pathfinding yang digunakan oleh NPC dalam game ini adalah Dynamic Pathfinding Algorithm (DPA) untuk menghindari hambatan statis dan dinamis di lintasan dan Algoritma A* yang digunakan untuk mencari rute terpendek pada lintasan. Hasil percobaan menunjukkan bahwa NPC yang menggunakan gabungan DPA dan Algoritma A* mendapatkan hasil yang lebih baik dari NPC yang hanya menggunakan Algoritma DPA saja, sedangkan posisi rintangan dan bentuk lintasan memiliki pengaruh yang besar terhadap DPA.
Adaptive Hint Generation for Educational Games Using Fuzzy Logic Primanita, Anggina; Satria, Hadipurnawan; Rizqie, Muhammad Qurhanul; Iskandar, Ananda Haykel; Nugraha, Wibisena
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.41893

Abstract

The increasing interest in programming education has led to a wide variety of learner abilities. However, existing learning media often remain fragmented, necessitating the development of adaptive tools to cater to learners of varying skill levels. This study employs fuzzy logic to generate dynamic hints for players struggling to solve programming challenges in an educational game. The effectiveness of the system was evaluated through both simulation and real-world experiments. Simulation results indicate that the fuzzy logic system successfully generates personalized hints, with the highest frequency of hints provided to beginner players. Real-world testing using the GUESS-18 framework demonstrated high playability and excellent usability scores for the game.
Co-Authors Abdiansah Abdiansah Abdiansah, Abdiansah Abdurahman Abdurahman Ahmad Fali Oklilas Ahmad Nizar Ahmad Zarkasi Ananda Haykel Iskandar Athalaza, Muhammad Nawar Danny M. Saputra Darmawijoyo, Darmawijoyo Dedy Fitriady Fitriady Dian Palupi Rini Egga Asoka Ermatita - Fathoni Fathoni Frendredi Muliawan Hadi Purnawan Satria Hadi Purnawan Satria Hadipurnawan Satria Hadipurnawan Satria Hasby Rifky Idawati Idawati Isa, Indra Griha Tofik Iskandar, Ananda Haykel Jambak, Muhammad Ihsan M. Fachrurrozi . M. Nazhif Rizani Muhammad Afif Muhammad Fachrurrozi Muhammad Nawar Athalaza Muhammad Qurhanul Rizqie Muhammad Qurhanul Rizqie Muhammad Rasuandi Nugraha, Wibisena Osvari Arsalan Rafly Pakomgan Ramadhan, Muhammad Fajar Rizki Kurniati Rusdi Efendi Samsuryadi Samsuryadi Saparudin Saparudin Saputra, Tommy Sri Desy Siswanti Sutarno Sutarno Sutarno Sutarno Sutarno Syahroyni, Muhammad Syamsul Arifin Syamsuryadi Syamsuryadi Wibisena Nugraha