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All Journal Jurnas Nasional Teknologi dan Sistem Informasi JTERA (Jurnal Teknologi Rekayasa) Applied Technology and Computing Science Journal Jurnal Teknologi Informasi dan Multimedia bit-Tech Jurnal Informasi dan Teknologi Jurnal Informatika dan Rekayasa Perangkat Lunak G-Tech : Jurnal Teknologi Terapan TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknologi Informatika dan Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) IICS KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Pengabdian Kepada Masyarakat Abdi Putra International Journal Engineering and Applied Technology (IJEAT) Jurnal Sains Informatika Terapan (JSIT) Jurnal Pengabdian Masyarakat Bhinneka SmartComp The Eastasouth Journal of Information System and Computer Science Jurnal Informatika Polinema (JIP) Aksi Nyata : Jurnal Pengabdian Sosial dan Kemanusiaan Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
IVANA LUCIA KHARISMA
Nusa Putra University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Genre-Based Anime Recommendation System Using KNN with Fanbase Bias Detection Muhamad Rizky Fauzi; Imam Sanjaya; Ivana Lucia Kharisma
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2917

Abstract

The rapid growth of the anime industry presents a challenge for users, especially newcomers, in finding content that matches their personal preferences. To address this issue, this study proposes a genre-based anime recommendation system using a Content-Based Filtering approach, incorporating Term Frequency-Inverse Document Frequency (TF-IDF), the K-Nearest Neighbor (KNN) algorithm, and fanbase bias detection. This system transforms genre information into numerical vectors using TF-IDF, allowing for precise similarity calculations between anime titles based on genre relevance. KNN is used with cosine similarity to identify the top five most similar anime to a given input. A key novelty of this study is the implementation of a fanbase bias detection mechanism that filters out anime with high ratings but very low member counts, which often distort overall ratings due to a small but passionate fanbase. This filtering process ensures that the recommendation output better reflects general audience preferences. The dataset, sourced from MyAnimeList via Kaggle, includes 12,294 entries and underwent extensive preprocessing, including missing value removal, duplicate elimination, and statistical thresholding for bias detection. Evaluation of the system was performed using accuracy, precision, recall, and F1-score, with results showing strong performance (F1-score of 91.94%). Additionally, 5-fold cross-validation confirmed the consistency of the model. Designed for general anime viewers, the system is implemented using the Streamlit framework to provide an accessible and interactive web-based interface. This study demonstrates that the combination of content-based techniques and fanbase bias filtering significantly enhances recommendation quality, offering a novel and practical solution for anime discovery
Utilization Of Content-Based Filtering Method In Game Recommendation With Support Vector Machine Algorithm Indra Yustiana; Ivana Lucia Kharisma; Ade Arian
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2966

Abstract

The explosive growth of the gaming industry has led to a "paradox of choice," where an overwhelming number of titles on platforms like Steam makes it difficult for players to discover games aligned with their personal preferences. This study addresses the critical challenge of game discovery by developing a novel recommendation system that integrates a Content-Based Filtering (CBF) approach with the Support Vector Machine (SVM) algorithm, a combination not extensively explored in the gaming domain. The system provides accurate, attribute-driven recommendations to enhance user experience. Utilizing a dataset of over 55.691 Steam games, we processed textual data such as genre, tags, and categories using TF-IDF before applying the SVM classifier. To validate its effectiveness, the model was benchmarked against K-Nearest Neighbor (KNN) across various training-to-testing ratios. The results demonstrate SVM's consistent superiority, achieving up to 98% accuracy. Notably, the high F1-score of 97.94% in genre-based recommendations signifies a well-balanced model that excels at both minimizing irrelevant suggestions and identifying relevant titles, directly translating to higher user satisfaction. The successfully deployed system, built on the Streamlit framework, was validated through black-box testing, confirming its functionality. This research confirms that the CBF-SVM model offers a highly effective solution to the game discovery problem, with future potential to incorporate hybrid filtering techniques for even greater personalization.
Pengembangan Sistem Klasifikasi Citra Daging Sapi Dan Daging Babi Berbasis Web Menggunakan DENSENET-121 Siti Nurviatika; Ivana Lucia Kharisma; Nugraha
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1176

Abstract

The circulation of beef and pork products that are difficult to distinguish visually can create challenges for consumers, making an automated meat identification system necessary. This study aims to develop an image classification model for beef and pork using the Convolutional Neural Network (CNN) method with the DenseNet-121 architecture and to implement it in a Streamlit-based web application. The dataset used in this study consists of 6,000 images, comprising 3,000 beef images and 3,000 pork images collected from two different dataset sources. The dataset underwent several preprocessing stages, including resizing, contrast enhancement, normalization, and data augmentation, and was subsequently divided into training, validation, and testing sets with a ratio of 70:15:15. The results show that the DenseNet-121 model is capable of classifying beef and pork images with excellent performance. Based on the evaluation using a confusion matrix and classification report, the model achieved an accuracy of 97.89%, with high precision, recall, and F1-score values for both classes. The trained model was then deployed in a web application that allows users to perform classification through image uploads or direct image capture using a camera. Based on these findings, it can be concluded that the DenseNet-121 architecture is capable of classifying beef and pork images with high accuracy and has the potential to be utilized as a practical tool for meat type identification.
Website-Based Ergonomic Sitting Posture Detection Using YOLOV8 Pose Estimation Nurazizah Zahra; Ivana Lucia Kharisma; Somantri
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1177

Abstract

This study aims to develop a web-based ergonomic sitting posture detection system to reduce postural fatigue caused by prolonged computer use. The proposed system uses a deep learning-based pose estimation method to detect body keypoints and calculate the user's posture angles. The dataset used consists of hundreds of images that have been enhanced in quality and quantity through a data augmentation process. The system then classifies sitting postures into ergonomic and non-ergonomic categories. Test results show that the system is able to achieve a high level of accuracy, with the model achieving 98% Precision, 99% Recall, 99% mAP50, and 82% mAP50-95 in detecting and classifying sitting postures. Furthermore, the web-based implementation allows for real-time monitoring. The results of this study indicate that computer vision technology has the potential to be an effective solution to increase awareness of correct sitting posture and help prevent postural fatigue, especially in academic environments.
Implementasi Teknologi YOLOv8n dan IoT pada Alat Pengusir Hama Burung dengan Raspberry Pi Bertenaga Solar Panel Ivana Lucia Kharisma; Kamdan Kamdan; Gina Purnama Insany; Asep Rizki Firdaus; Dendi Nasrulloh
JTERA (Jurnal Teknologi Rekayasa) Vol 10 No 2: December 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.35-44

Abstract

Hama burung adalah salah satu tantangan serius yang dihadapi pada sektor pertanian. Metode tradisional yang dilakukan untuk mengatasi hama burung hanya memberikan solusi sementara dan juga mengakibatkan petani tidak dapat melakukan aktifitas lainnya, karena harus menjaga sawah secara terus menerus. Untuk optimalisasi serta untuk mengatasi permasalahan yang muncul pada perancangan alat sebelumnya yaitu kendala pada koneksi internet yang tidak stabil, sumber listrik yang terbatas serta permasalahan penyimpanan lokal pada alat , dibutuhkan integrasi antara konsep kecerdasan buatan (AI) dan Internet of Thing (IoT). Pengembangan alat menggunakan model deteksi objek versi YOLOv8nano, Raspberry Pi sebagai Single Board Computer dan penambahan solar panel . Pengujian yang dilakukan secara langsung menunjukkan bahwa fungsionalitas dan kinerja alat telah sesuai dengan yang diharapkan. Alat mampu mendeteksi objek burung dengan kinerja nilai rata-rata confidence score 74,64%, sedangkan pada pengujian solar panel menunjukkan hasil daya listrik yang mampu disimpan dalam durasi waktu 12 rata-rata sebesar 13,31 volt. Penambahan sebuah dashboard monitoring, bermanfaat untuk memberikan informasi kinerja alat.  Terpenuhinya semua proses pengujian menunjukkan bahwa alat pengusir burung ini memiliki potensi besar untuk memudahkan petani dalam mengatasi masalah hama burung secara lebih efektif, sehingga mengurangi ketergantungan pada metode tradisional.
Implementasi Multimodal Emotion Recognition Menggunakan CNN-BiLSTM-Attention pada Audio dan Mobilenetv2 pada Ekspresi Wajah Ai Solihah; Alun Sujjada; Ivana Lucia Kharisma
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10629

Abstract

The advancement of artificial intelligence technology has significantly contributed to the development of emotion recognition systems capable of understanding human emotional states more accurately. However, unimodal approaches that rely on a single data source, such as speech or facial expressions, still face limitations when dealing with dynamic environmental conditions. This study aims to implement a Multimodal Emotion Recognition system by integrating audio and visual modalities to improve emotion detection accuracy. The audio modality is processed using a CNN-BiLSTM-Attention architecture with Mel-Spectrogram representations as input, while the visual modality is analyzed using MobileNetV2 to recognize facial expressions. The integration of both modalities is performed using the Late Fusion method at the decision level through a weighted fusion approach, assigning weights of 0.4 and 0.6 to the audio and visual modalities, respectively. This research employs an experimental method with a quantitative approach. The research stages include literature review, data collection, audio and video preprocessing, model design, model training, multimodal integration, and system evaluation. Audio data are transformed into Mel-Spectrogram features, while video data are processed using MediaPipe Face Detection to extract facial regions prior to emotion classification. The proposed system is designed to recognize three emotional classes: neutral, happy, and angry. Model performance is evaluated using a Confusion Matrix, accuracy, precision, recall, and F1-score metrics. The results indicate that multimodal integration using the Late Fusion method effectively leverages the strengths of each modality, resulting in more accurate and robust emotion detection compared to unimodal approaches. Furthermore, the developed system is capable of performing real-time emotion classification through a camera and microphone, making it potentially applicable in various domains, including education, customer service, human-computer interaction systems, and user emotional state monitoring. The primary contribution of this research is the development of a multimodal emotion recognition system that integrates a CNN-BiLSTM-Attention model for the audio modality and MobileNetV2 for the visual modality using a late fusion method. The proposed system leverages emotional information from both modalities simultaneously to enhance the accuracy and stability of real-time emotion detection, thereby offering an alternative solution for the development of AI-based human-computer interaction systems.
Deteksi Ulasan Palsu Berbahasa Indonesia Menggunakan Model DistilBERT: Studi Kasus pada Platform E-Commerce Nurani Istiaen; Ivana Lucia Kharisma; Somantri Somantri; Kamdan Kamdan; Elsy Rahajeng
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 3 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v15i3.9669

Abstract

Kemajuan teknologi telah mempermudah berbagai aspek kehidupan manusia, termasuk di sektor bisnis dan ekonomi. Kehadiran e-commerce memungkinkan interaksi antara penjual dan pembeli berlangsung lebih efektif serta efisien. Dalam proses pengambilan keputusan pembelian, ulasan pelanggan memiliki peran penting sebagai sumber informasi mengenai kualitas produk maupun layanan. Oleh karena itu, keaslian ulasan sangat krusial dalam menjaga kepercayaan pengguna sekaligus integritas platform.Penelitian ini menggunakan dataset berisi 887 ulasan berbahasa Indonesia yang dikumpulkan dari salah satu toko di satu platform e-commerce. Sistem deteksi ulasan palsu dibangun dengan memanfaatkan model DistilBERT berbasis pemrosesan bahasa alami (NLP) yang diawali dengan tahapan preprocessing. Model dilatih dalam beberapa skenario, baik tanpa maupun dengan teknik oversampling, lalu dievaluasi menggunakan accuracy, precision, recall, F1-Score, confusion matrix, serta ROC-AUC. Hasil evaluasi menunjukkan bahwa oversampling memberikan performa terbaik dengan akurasi 97% dan ROC-AUC 0.97–0.99. Model mampu mengenali ulasan asli (OR) dengan presisi 0.99 serta mendeteksi ulasan palsu (CG) dengan recall 0.96. Pada skenario 50 epoch, ROC-AUC tertinggi dicapai dengan nilai 0.9921. Selanjutnya, model diimplementasikan ke dalam aplikasi web berbasis Streamlit untuk membantu pengguna mengambil keputusan pembelian yang lebih bijak di e-commerce.Kata Kunci – Ulasan Palsu; DistilBERT; Pemrosesan Bahasa Alami; Perdagangan Elektronik; Streamlit
Implementasi Metode Fuzzy Logic Mamdani Untuk Rekomendasi Takaran Kombinasi Buah Penderita Diabetes Kania Purnarahayu; Indra Yustiana; Ivana Lucia Kharisma
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1203

Abstract

Diabetes mellitus is a chronic metabolic condition that requires proper dietary management to keep blood glucose levels stable. Fruit is recommended for people with diabetes as it contains important nutrients; however, differences in carbohydrate and fibre content mean that careful consideration must be given to the choice of fruit combinations and portion sizes. This study aims to apply the Mamdani fuzzy logic inference method to generate recommendations for fruit combination portions based on carbohydrate and fibre content. Secondary data were obtained from the Indonesian Food Composition Table (TKPI), which comprises 10 types of fruit. Data processing involved fuzzification, the formulation of a rule base, Mamdani inference using the Min-Max operator, and defuzzification using the centroid method. The defuzzification results yield a compatibility value which is used to determine the recommendation category and the appropriate fruit combination portion. This method has been implemented in a web-based application to provide automatic recommendations to users. The research findings indicate that the Mamdani fuzzy logic inference method can effectively process carbohydrate and fibre content into measurable and easily understandable recommendations regarding fruit combination portions. The proposed system can assist people with diabetes in selecting suitable fruit combinations and appropriate portion sizes, thereby supporting healthier dietary planning and facilitating daily decision-making regarding fruit consumption.
Application of Artificial Neural Network in Estimating Harvest Time of Lettuce and Spinach Plants in Nutrient Film Technique Hydroponic System Zilfa Agustina Munawar; Gina Purnama Insany; Ivana Lucia Kharisma
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3261

Abstract

Hydroponic farming using the Nutrient Film Technique (NFT) system is widely implemented due to its efficiency in nutrient management and water use. Spinach and lettuce are leafy commodities widely cultivated using this system because they have a relatively short growth cycle and high economic value. However, determining harvest time is still often done manually based on experience, potentially leading to inaccurate decisions that impact the quality and quantity of production. This study aims to develop a prediction model for harvest time for hydroponic spinach and lettuce plants based on Artificial Neural Network (ANN) by utilizing environmental and physiological parameters of the plant. The parameters used include water temperature, air humidity, light intensity, pH, Electrical Conductivity (EC), and plant age. The dataset used consists of 1,200 observation data of NFT hydroponic cultivation results from January to July 2025. The data went through a preprocessing stage in the form of cleaning, normalization, and dividing training data and test data with a ratio of 80:20. The ANN model was built using the backpropagation method with training parameter optimization. Data was obtained from plant growth monitoring, then normalized and divided into training and test data. Test results showed a prediction accuracy of 92.8% based on MAPE, MAE, and R-squared. This model was implemented in a Streamlit-based web application to facilitate farmer use, making harvest timing more objective, measurable, and data-driven.
Implementation of Enterprise Resource Planning (ERP) Based Information System Using Odoo Software Mega Putri Utami; Ivana Lucia Kharisma
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.161

Abstract

Management and good data management is something that is very important for the continuity of a company. With management in a company, it is expected that all actions or activities carried out will run well and be controlled. The research focused on the use of an Enterprise Resource Planning (ERP) Based Information System using the odoo software used in the Purchasing & Materials department or more specifically known as PPIC. The purpose of this research is to find out how the use of ERP technology can facilitate coordination and communication between users so as to produce fast decision making. The implementation of this information system is expected to be one of the solutions in overcoming the problems that exist at PT Longvin Indonesia, especially the PPIC department.
Co-Authors adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Ade Arian Adhitia Erfina Adisti Ridha Ramadhan Ai Solihah Algifari, M. Alwan Alida Fany Tariza Putri Alun Sujjada Alun Sujjada Alyanissa Putri Iskandar Andi Agusti Andi Nopiandi Andi Nopiandi Armelia Isabela Taek Asep Rizki Firdaus Asep Rizki Firdaus Atikah Mugiyanti Azkal Khalif Bilqis Zahra Dede Serlina Dendi Nasrulloh Dendi Nasrulloh Dewi Puspitasari Dhea Ayu Septiani Dhea Ayu Septiani Dila Aura Futri Dwi Sartika Simatupang E. Tesly Navida Elsy Rahajeng Fakhriyal Riyandi Yasin falentino sembiring Falya Amrina Zahra Fransiskus Octavianus Mado Hurint Galih Rakasiwi Galuh Ratna Putri Gina Purnama Insany Gina Purnama Insany Gina Purnama Insany Hermanto Ika Imam Sanjaya Indra Yustiana Indra Yustiana Ira Rohimah Junjun Junaedi Kamdan Kamdan Kamdan Kania Purnarahayu Lufita Alvira Maximillian Huang Mayang Selpiyana Mega Putri Utami Meutia Riany Meylinda Nuryani Mirna Kamilah Moh. Abd. Aziz Hidayat Muhamad Galih Sundayana Muhamad Rizky Fauzi Muhammad Dafik Kholik Firdaus Muhammad Ikhsan Thohir Muhammad Ikhsan Thohir Muhammad Raihan Asshafwat Muslih, Muhamad Naufal Nuryanto Neng Syahla Nida Khofifah Nieka Julyana Nugraha Nur Hidayah K Fadhilah Nurani Istiaen Nurazizah Zahra Paikun Pascal Aditia Muclis Purnama Insany, Gina Putri Anugrah S Putri Iskandar, Alyanissa Resma Nuraeni Rismi Nurlaely Rizki Haddi Prayoga ROSNIA YURISTA Saila Julia Sally Agustin Elisya Salman Alhidamkara Sany Noor Fauzianty Setiana Andika Putra Setiawati Siti Nurviatika Siti Sarah Sobariah Lestari Somantri Somantri Somantri Somantri Suhendar Suhendar Teguh Gumelar Teguh Gumelar Tofik Hidayat Tri Hadianto Widy Karisma Wigi Januar Rahman Wilda Widyana Yusup Solehudin Zilfa Agustina Munawar