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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik JUTI: Jurnal Ilmiah Teknologi Informasi Prosiding SNATIF Journal of ICT Research and Applications Teknika: Jurnal Sains dan Teknologi Jurnal Informatika dan Teknik Elektro Terapan Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer English Language and Literature International Conference (ELLiC) Proceedings Infotekmesin Jurnal Mnemonic Abdimasku : Jurnal Pengabdian Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Soft Computing Exploration Advance Sustainable Science, Engineering and Technology (ASSET) Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Advance Sustainable Science, Engineering and Technology (ASSET) INOVTEK Polbeng - Seri Informatika
Christy Atika Sari
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space Santoso, Bagus Raffi; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8916

Abstract

Coffee is a vital agricultural commodity, and precise classification of coffee beans is crucial for quality assessment and agricultural practices. In this study, we propose a methodology utilizing Convolutional Neural Networks (CNN) based on ResNet-101 architecture for coffee bean classification. The novelty of our approach lies in the integration of comprehensive feature extraction from grayscale coffee bean images, including mean, standard deviation, skewness, energy, entropy, and smoothness, with the transfer learning capabilities of CNN. Through this integration, we achieved exceptional classification performance, with the CNN model attaining accuracy, recall, precision, and F1-score metrics of 99.44% and 100% on the training data, and 100% on the testing data. These results underscore the robustness and generalization capability of our methodology in accurately classifying coffee bean types. While the dataset used in this study is experimental, the comprehensive feature extraction and the effectiveness of the CNN architecture suggest the potential for accurate classification of coffee bean types beyond the experimental data, provided the new data shares similar characteristics to the collected samples. For future research, we recommend exploring the integration of two transfer learning techniques within CNN architectures to further enhance coffee bean classification systems. Specifically, leveraging pre-trained CNN models as a foundation for feature extraction, while simultaneously fine-tuning specific layers to adapt to the nuances of coffee bean classification tasks, could offer improved model performance and scalability.
FOOTBALL PLAYER TRACKING, TEAM ASSIGNMENT, AND SPEED ESTIMATION USING YOLOV5 AND OPTICAL FLOW Hartono, Matthew Raymond; Sari, Christy Atika; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4165

Abstract

Football analysis is indispensable in improving team performance, developing strategy, and assessing the capabilities of players. A powerful system that combines YOLOv5 for object detection with optical flow tracks football players, assigns them to their respective teams, and estimates their speeds accurately. In the most crowded scenarios, the players and the ball are detected by YOLOv5 at 94.8% and 93.7% mAP, respectively. KMeans clustering based on jersey color assigns teams with 92.5% accuracy. Optical flow is estimating the speed with less than 2.3%. The perspective transformation using OpenCV improves trajectory and distance measurement, overcoming the challenges in overlapping players and changing camera angles. Experimental results underlined the system's reliability for capturing player speeds from 3 to 25 km/h and gave insight into the dynamic nature of team possession. However, there is still some challenge: 6% accuracy degradation in high overlap and illuminative changes. The future work involves expanding the dataset for higher robustness and ball tracking, which will comprehensively explain the dynamics of a match. The paper presents a flexible framework for automated football video analysis that paves the way for advanced sports analytics. This would also, in turn, enhance informed decision-making by coaches, analysts, and broadcasters by providing them with actionable metrics during training and competition. The proposed system joins the state-of-the-art YOLOv5 with optical flow and thereby forms the backbone of near-future football analysis.
The AirNav Semarang Employee Presence System Using Face Recognition Based on Haar Cascade Fidela Azzahra; Christy Atika Sari; Eko Hari Rachmawanto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.672

Abstract

The presence of employees is a key factor in supporting the needs of the workplace. At present, the employee presence system at PT. AirNav Indonesia Semarang Branch still uses fingerprint and RFID-based employee ID cards for authentication. This RFID-based system can increase employee fraud by allowing employees to misuse each other's ID cards. To avoid such fraud, a system needs to be built and it will be using face recognition technology as the primary authentication method, with the Haar Cascade Algorithm. This algorithm has the advantage of being computationally fast, as it only relies on the number of pixels within a rectangle, not every pixel of an image. In addition to fast computation, this algorithm also has the advantage of identifying objects that are relatively far away. With the implementation of the Haar Cascade algorithm, the results indicate the capability of face recognition in detecting the faces of registered employees within the system based on facial angles with an accuracy rate of 60%, expressions with an accuracy rate of 100%, as well as obstructive parameters such as glasses and masks with an accuracy rate of 33.33%. The ability to detect objects from various camera angles, recognize faces with different expressions, and identify objects obstructed by parameters can serve as reasons why this algorithm needs to be implemented
Implementation of DenseNet121 Architecture for Waste Type Classification Munis Zulhusni; Christy Atika Sari; Eko Hari Rachmawanto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.673

Abstract

The growing waste management problem in many parts of the world requires innovative solutions to ensure efficiency in sorting and recycling. One of the main challenges is accurate waste classification, which is often hampered by the variability in visual characteristics between waste types. As a solution, this research develops an image-based litter classification model using Deep Learning DenseNet architecture. The model is designed to address the need for automated waste sorting by classifying waste into ten different categories, using diverse training datasets. The results of this study showed that the model achieved an overall accuracy rate of 93%, with an excellent ability to identify and classify specific materials such as batteries, biological materials, and brown glass. Despite some challenges in metal and plastic classification, these results confirm the great potential of using Deep Learning technology in waste management systems to improve sorting processes and increase recycling efficiency
Classification of Movie Recommendation on Netflix Using Random Forest Algorithm Alifia Salwa Salsabila; Christy Atika Sari; Eko Hari Rachmawanto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.676

Abstract

Netflix is one of the most popular streaming platforms in this world. So many movies and shows with various genres and production countries are available on this platform. Netflix has their own recommendation systems for the subscribers according to their data and algorithm. This research aims to compare two methods of data classifications using Decision Tree and Random Forest algorithm and make a recommendation system based on Netflix dataset. This paper use feature importance to selecting relevant feature and how n_estimators affect the classification. In this research, Random Forest with 50 trees estimator with 96.84% accuracy before feature selection and 96.92% accuracy after feature selection has the best accuracy compared to the Decision Tree classification. Besides, Decision Tree has only 95.64% accuracy before feature selection and increases to 96.07% accuracy after feature selection. Trees estimator also affect the accuracy of Random Forest classification. After comparing the results, Random Forest with 50 trees estimators using feature selection provides best accuracy and it will be used to predict some similar movies and shows recommendation
A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network Salma Shafira Fatya Ardyani; Christy Atika Sari
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.771

Abstract

The diversity of Demak batik motifs presents challenges in classification and identification. This research aims to develop a Demak batik motif classification system using deep learning and VGG16 convolutional network. A dataset of Demak batik images is collected and processed to train the model. The VGG16 architecture is modified by fine-tuning to optimize the classification performance. Results show that the modified VGG16 model achieved a classification accuracy of 98.72% on the test dataset, demonstrating its potential application in preserving and digitizing Demak batik cultural heritage.
Classification of Corn Leaf Disease Using Convolutional Neural Network Ratih Ariska; Christy Atika Sari; Eko Hari Rachmawanto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.772

Abstract

Corn is a crop that plays a major role in food supply worldwide. Known as a cereal crop with high economic value, corn is one of the most important raw materials in the agricultural industry in many parts of the world. Leaf blight is characterized by small spots that gradually enlarge and turn brown. It is a decay of foliage caused by the fungus or species Rhizoctonia solani. Leaf spot is caused by the fungus Hel-minthoporium maydis, while stem rot is caused by Fusarium granearum. From these problems, a machine learning-based solution is given to classify corn leaf diseases using the Convolutional Neural Network (CNN) algorithm. CNN are used to classify corn leaf diseases. The selection of CNN is based on its ability to extract local attributes from image data and combine them for a more detailed and abstract representation, which is better. Classification was performed using 2145 datasets for leaf blight and 1574 datasets for leaf spot. The accuracy results obtained from this study reached 99% with the last training accuracy value of 99.06% and the last validation accuracy result of 98.50%. For future research may use more modern architectures such as classification using EfficientNet B3 architecture with transfer learning or MobileNet to improve accuracy results.
Jasmine Flower Classification with CNN Architectures: A Comparative Study of NasNetMobile, VGG16, and Xception in Agricultural Technology Danar Bayu Adi Saputra; Christy Atika Sari; Eko Hari Rachmawanto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.790

Abstract

Jasmine flowers have many benefits and uses such as for traditional medicine, tea, perfume, cosmetics, decoration, and others. in the selection of fresh jasmine flowers for making tea is very important, currently the classification of jasmine flowers for making tea is mostly still using manual methods. Often influenced by individual preferences, opinions, or biases. this causes a lack of objectivity and uncertainty in the classification of jasmine flowers. The manual method is very weak due to human visual limitations and fatigue levels which can result in less than the optimal jasmine flower classification. Therefore, in the research that has been done, a transfer learning system was applied that can classify fresh jasmine flowers with rotten jasmine flowers. This study aims to compare three different Convolutional Neural Network architectures: NasNetMobile, VGG16, and Xception. The results on the three architectures can show maximum results, namely 99.21% for NasNetMobile, 98.69% for VGG16 and 97.91% for Xception. This study provides insight into the classification of good and bad jasmine flowers to encourage further exploration in the field of agriculture.
Implementasi E-Arsip Untuk Penyimpanan Dokumen Digital Pada PT BPD Jateng (Bank Jateng) Nilawati, Florentina Esti; Rizal, Mohammad; Rachmawanto, Eko Hari; Setiadi, De Rosal Ignatius Moses; Sari, Christy Atika
Techno.Com Vol. 18 No. 4 (2019): November 2019
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1058.177 KB) | DOI: 10.33633/tc.v18i4.2508

Abstract

Bank Pembangunan Daerah Jawa Tengah merupakan Bank milik Pemerintah Provinsi Jawa Tengah bersama-sama dengan Pemerintah Kota/Kabupaten Se-Jawa Tengah. Seiring dengan kemajuan teknologi pengarsipan di Bank Pembangunan Daerah Jawa Tengah memerlukan digitalisasi dalam hal pengarsipan data. Pengarsipan data adalah proses memindahkan data yang tidak aktif lagi digunakan ke perangkat penyimpanan terpisah untuk jangka panjang. Data arsip terdiri dari data lama, serta data yang harus dipertahankan untuk kepatuhan terhadap peraturan. Arsip data diindeks dan memiliki kemampuan pencarian sehingga file dan bagian file dapat dengan mudah ditemukan dan diambil. Backup data digunakan sebagai mekanisme pemulihan data yang bisa digunakan untuk mengembalikan data jika rusak atau hancur. Sebaliknya, arsip data melindungi informasi lama yang tidak diperlukan untuk operasi sehari-hari namun mungkin harus diakses sesekali. Arsip data berfungsi untuk mengurangi konsumsi penyimpanan primer dan biaya terkait, daripada bertindak sebagai mekanisme pemulihan data. Beberapa arsip data memperlakukan data sebagai read-only untuk melindunginya dari modifikasi, sedangkan data lain produk pengarsipan memperlakukan data sebagai read / write. Dengan dibangunnya e-Arsip pada PT. Bank Pembangunan Daerah Jawa Tengah (Bank Jateng) diharapkan di kemudian hari untuk proses pencarian dan penyimpanan data dapat dilakukan dengan mudah, sehingga dapat menghemat waktu dan tenaga pada proses pencarian arsip.
Schizophrenia Classification using Fuzzy K-Nearest Neighbour on Patient Data from RSJD Dr. Amino Gondohutomo Ozagastra Caluella Prambudi; Ajib Susanto; Christy Atika Sari
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/t2mfvf14

Abstract

Schizophrenia is a complex mental disorder with overlapping symptoms, making subtype diagnosis uncertain. This study aims to develop an automated classification method for schizophrenia subtypes using the Fuzzy K-Nearest Neighbour (FKNN) algorithm, which effectively handles uncertainty in medical data. The dataset includes 300 patients from RSJD Dr. Amino Gondohutomo, Central Java, aged 18–60 years, with balanced gender distribution. Four subtypes—paranoid, catatonic, hebephrenic, and undifferentiated—were classified. Symptom and demographic data were encoded and normalised using min-max scaling. The model was trained using k = 5 and evaluated via 10-fold cross-validation. The results achieved 94% accuracy with high precision and recall across all classes. However, limitations include a relatively small and single-source dataset and the lack of ROC/AUC analysis. These findings suggest that FKNN has strong potential as a data-driven decision support system for schizophrenia diagnosis, suitable for integration into psychiatric hospital information systems. Future research should explore oversampling techniques such as SMOTE and threshold tuning to improve model sensitivity.
Co-Authors AA Sudharmawan, AA Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abiyyi, Ryandhika Bintang Ahmad Salafuddin Ajib Susanto Akbar, Fadhilah Aditya Akbar, Ilham Januar Alfany, Fauzan Maulana Ali, Rabei Raad Alifia Salwa Salsabila Alvian Ideastari, Nukat Alvin Faiz Kurniawan Anak Agung Gede Sugianthara Andi Danang Krismawan Anggraeny, Tiara Anidya Nur Latifa Annisa Sulistyaningsih Anny Yuniarti Antonius Erick Handoyo Arditya Prayogi Ardyani, Salma Shafira Fatya Arfian, Aldi Azmi Ariska, Ratih Aristides Bima Wintaka Aryanta, Muhammad Syifa Aryaputra, Firman Naufal Astuti, Yani Parti Auni, Amelia Gizzela Sheehan Azzahra, Fidela Bambang Sugiarto Briliantino Abhista Prabandanu Budi Harjo Cahaya Jatmoko Cahyo, Nur Ryan Dwi Candra Irawan Candra Irawan Castaka Agus Sugianto Chaerul Umam Chaerul Umam Cinantya Paramita D.R.I.M. Setiadi Danang Krismawan, Andi Danang Wahyu Utomo Danar Bayu Adi Saputra Danu Hartanto Daurat Sinaga Daurat Sinaga De Rosal Ignatius Moses Setiadi Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Didik Hermanto Doheir, Mohamed Doheir, Mohamed Doheir, Mohamed A S Dwi Puji Prabowo Edi Faisal Egia Rosi Subhiyakto Egia Rosi Subhiyakto Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Ericsson Dhimas Niagara Erika Devi Udayanti Erlin Dolphina Erna Daniati Erna Zuni Astuti Ery Mintorini Etika Kartikadarma Farrel Athaillah Putra Feri Agustina Fidela Azzahra Florentina Esti Nilawati Florentina Esti Nilawati Florentina Esti Nilawati Folasade Olubusola Isinkaye Folasade Olubusola Isinkaye Giovani Ardiansyah Gumelar, Rizky Syah Guruh Fajar Shidik Gusta, Muhammad Bima Hadi, Heru Pramono Haqikal, Hafidz Hartono, Matthew Raymond Haryanto, Christanto Antonius Haryanto, Christanto Antonius Hasbi, Hanif Maulana Hayu Wikan Kinasih Heru Lestiawan Himawan, Reyshano Adhyarta Hussain Md Mehedul Islam Hyperastuty, Agoes Santika Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ifan Rizqa Ihya Ulumuddin, Dimas Irawan Ikhsanuddin, Rohmatulloh Muhamad Imam Prayogo Pujiono Inzaghi, Reza Bayu Ahmad Iqtait, Musab Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul Istiqomah, Annisa Ayu Ivan Stepheng Kamila, Izza Putri Kas Raygaputra Ilaga Krismawan, Andi Danang Kumala, Raffa Adhi Kurniawan, Nicholas Alfandhy Kusuma, Edi Jaya Kusuma, Mohammad Roni Kusumawati, Yupie L. Budi Handoko Laksana, Deddy Award Widya Lalang Erawan Liya Umaroh Liya Umaroh, Liya Lucky Arif Rahman Hakim Mabina, Ibnu Farid Maulana Malik Ibrahim Al-Ghiffary Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Mehta Pradnyatama Meitantya, Mutiara Dolla Mohamed Doheir Mohamed Doheir Mohammad Rizal, Mohammad Mohd Yaacob, Noorayisahbe Muchamad Akbar Nurul Adzan Muhammad Rikzam Kamal Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Munis Zulhusni Musfiqur Rahman Sazal Muslih Muslih Nabila, Qotrunnada Naufal, Muhammad Khanif Neni Kurniawati Ningrum, Amanda Prawita Nisa, Yuha Aulia Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Noorayisahbe Mohd Yacoob Nova Rijati Nugroho, Widhi Bagus Nur Ryan Dwi Cahyo Oktaridha, Harwinanda Oktayaessofa, Eqania Ozagastra Caluella Prambudi Parti Astuti, Yani Parti Astuti, Yani parti astuti, yani Parti Astuti1, Yani Parti Astuti1, Yani Permana langgeng wicaksono ellwid putra Pradana, Luthfiyana Hamidah Sherly Pradana, Rizky Putra Praskatama, Vincentius Pratama, Zudha Pratiwi, Saniya Rahma Pulung Nurtantio Andono Purwanto Purwanto Puspa, Silfi Andriana Putri Mega Arum Wijayanti Rabei Raad Ali Rabei Raad Ali Rahmalan, Hidayah Raisul Umah Nur Ramadhan Rakhmat Sani Ratih Ariska Rizky Damara Ardy Robert Setyawan Sabilillah, Ferris Tita Saifullah, Zidan Salma Shafira Fatya Ardyani Salsabila, Alifia Salwa Sania, Wulida Rizki Santoso, Bagus Raffi Saputra, Danar Bayu Adi Sari, Wellia Shinta Sari Shinta Sarker, Md Kamruzzaman Sarker, Md. Kamruzzaman Setiarso, Ichwan Setiawan, Fachruddin Ari Shelomita, Viki Ari Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sofyan, Ega Adiasa Solichul Huda, Solichul Sudibyo, Usman Sudibyo, Usman Sudibyo, Usman Sumarni Adi, Sumarni Suprayogi Suprayogi Suprayogi Suprayogi Sutrisno, Hendra Syabilla, Mutiara Tan Samuel Permana Tan Samuel Permana Tiara Anggraeny Titien Suhartini Sukamto Umah Nur, Raisul Umaroh, Liya Umaroh, Liya Utomo, Danang Wahyu Velarati, Khoirizqi Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yani Parti Astuti Yupie Kusumawati Zaenal Arifin Zahra Ghina Syafira Zulhusni, Munis