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All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA) TELKOMNIKA (Telecommunication Computing Electronics and Control) Abdimas Lembaran Ilmu Kependidikan Scientific Journal of Informatics Jurnal Kajian Informasi & Perpustakaan JURNAL PROFESI KEGURUAN Dinamisia: Jurnal Pengabdian Kepada Masyarakat JMM (Jurnal Masyarakat Mandiri) Journal of Innovation in Educational and Cultural Research Journal of Soft Computing Exploration International Journal of Engineering, Science and Information Technology Journal of Advances in Information Systems and Technology Proceeding of International Conference on Science, Education, and Technology Recursive Journal of Informatics Scientific Journal of Informatics Jurnal Abdi Negeri Recursive Journal of Informatics
Aji Purwinarko, Aji
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Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Implementation of Synthetic Minority Oversampling Technique and Two-phase Mutation Grey Wolf Optimization on Early Diagnosis of Diabetes using K-Nearest Neighbors Fathan Arsyadani; Aji Purwinarko
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/s6qsz079

Abstract

Abstract. Diabetes is a disease attacking the endocrine system characterized by high blood sugar levels. International Diabetes Federation (IDF) estimates that there were 451 million people with diabetes globally in 2017. Without treatment, this number is expected to rise to 693 million by 2045. One method for preventing increases in the number of diabetics is by early diagnosis. In an era where technology has developed rapidly, early diagnosis can be made with the machine learning method using classification. In this study, we propose a diabetes classification using K-Nearest Neighbors (KNN). Before classifying the data, we select the best feature subset from the dataset using Two-phase Mutation Grey Wolf Optimization (TMGWO) and balance the training data using Synthetic Minority Oversampling Technique (SMOTE). After dividing the dataset into training and testing sets using 10-fold cross validation, we reached an accuracy of 98.85% using the proposed method. Purpose: This study aims to understand how to apply TMGWO and SMOTE to classify the early stage diabetes risk prediction dataset using KNN and how it affects the results. Methods/Study design/approach: In this study, we use TMGWO to make a feature selection on the dataset, K-fold cross validation to split the dataset into training and testing sets, SMOTE to balance the training data, and KNN to perform the classification. The desired results in this study are accuracy, precision, recall, and f1-score. Result/Findings: Performing classification using KNN with only features selected by TMGWO and balancing the training data using SMOTE gives an accuracy rate of 98.85%. From the results of this research, it can be concluded that the proposed algorithm can give higher accuracy compared to previous studies. Novelty/Originality/Value: Implementing TMGWO to perform feature selection so the model can perform classification with fewer features and implementing SMOTE to balance the training data so the model can better classify the minority class. By doing classification using fewer features, the model can perform classification with a shorter computational time compared to using all features in the dataset.
Improving Sentiment Analysis with a Context-Aware RoBERTa–BiLSTM and Word2Vec Branch Wahyu Hardyanto; Nila Prasetya Aryani; Defin Andestian; Sugiyanto; Wahyu Setyaningrum; M Fadil Mardiansyah; Muhamad Anbiya Nur Islam; Aji Purwinarko
Scientific Journal of Informatics Vol. 12 No. 4: Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35918

Abstract

Purpose: We improve the accuracy of Twitter/X sentiment analysis with a hybrid model combining Word2Vec and the Robustly Optimized BERT Pretraining Approach (RoBERTa). However, Twitter/X text is noisy (slang/OOV) and ambiguous, so the performance of the pre-trained transformer decreases. Word2Vec is also limited to local contexts. Integrative studies of both are still limited. The idea is that Word2Vec is strong for slang/novel vocabulary (distributional semantics), while RoBERTa excels in contextual meaning; combining the two mitigates each other's weaknesses. Methods: The Sentiment140 dataset contains 1.6 million balanced tweets. The split is stratified; Word2Vec is trained solely on the training data. RoBERTa is pretrained (frozen in the first stage, then fine-tuned with some layers in the second stage). The Word2Vec and RoBERTa vectors are concatenated and processed using Bidirectional Long Short-Term Memory (BiLSTM) with sigmoid activation. Training utilizes TensorFlow and the Adam optimizer, incorporating dropout and early stopping. The decision threshold is optimized during the validation process. Result: The hybrid model achieved an accuracy of 88.09%, an F1-score of 88.09%, and an Area Under the Curve (AUC) ≈ 95.19% on the Receiver Operating Characteristic (ROC). No overfitting was observed, and the hybrid model outperformed both single baselines. The confusion matrix and ROC curve corroborate the findings. Novelty: The novelty lies in the fusion of distributional and contextual representations with a structured fusion mechanism. Limitations: Computational requirements and hyperparameter tuning are not yet extensive. Further directions: Systematic hyperparameter search and cross-validation across other large sentiment datasets to assess generalization.
The Radio Frequency Identification Implementation Design for INLISLite Library Management System Majidah Majidah; Widiyanto Widiyanto; Aji Purwinarko; Kasinyo Harto; Fridiyanto Fridiyanto; Amirul Mukminin
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.902

Abstract

A challenge with self-borrowing services is the investment cost for both software and hardware. A software license is required to connect INLIS [Integrated Library System] Lite with Radio Frequency Identification [RFID], which results in a high cost, especially when the software is only compatible with specific RFID devices. This study aims to look at the INLISLite Library Information System, focusing on implementing a self-borrowing service using microcontrollers and RFID technology. In this research, system development uses the prototyping method. This study developed a self-borrowing module for INLISLite without the need for licensed connector software. Additionally, the module is compatible with various microcontrollers and RFID devices that are readily available. The research proposes a novel model that utilizes RFID technology and NodeMCU ESP8266 for the INLISLite Library Information System. RFID sensors read book data from tags, while the NodeMCU microcontroller facilitates communication between the RFID system and the server, allowing automatic transmission of book data to the INLISLite database. This setup enables seamless self-service borrowing, which was tested successfully, supporting processes such as logging in, scanning book data, and updating loan status in the database.
Thermal Durability Characterization of a Simple Polymethyl-methacrylate (PMMA) Based-Optical Waveguide Ian Yulianti; Shiva Maulana Khoiru Insan; Ngurah Made Darma Putra; Aji Purwinarko; Nuni Widiarti; Nor Hafizah Ngajikin
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol. 14 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v14n2.p113-124

Abstract

Polymethyl-methacrylate (PMMA)-based optical waveguide is a good candidate for a simple and low-cost waveguide. However, the thermal properties have not been investigated. In this work, thermal durability characterization of PMMA-based waveguide was carried out. Waveguide fabrication process was done in three stages, which are patterning the PMMA cladding, core material synthetization and core material application to the cladding. Core pattern with cross section area of 1×1 mm2 was engraved on the 4 cm long PMMA sheet. Unsaturated polyester resin (UPR) was used as a core material. Characterizations were conducted for temperature dependent loss (TDL), temperature working range, and long exposure durability. For TDL characterization, the temperature varied from 30°C to 75°C. Meanwhile, for temperature working range, the waveguide was exposed to cycled heating. The thermal durability characterization was done by immersing the waveguide in distilled water at temperature of 40 °C for 288 hours. The results showed that a little change of output intensity occurred due to temperature variation with TDL of 0.0235 dB/°C. The maximum limit of the temperature is 70°C. For long exposure to temperature of 40oC, the results showed that the waveguide has a good performance.
Co-Authors Adi, Mahargjo Hapsoro Aditya Marianti Akhmad Munawar Alfa Narendra Alfandy Himawan Bagus Rafli Amalia Langgundi, Fitri Amidi Amidi Amin Retnoningsih Amirul Mukminin Anggyi Trisnawan Putra Annindya Ardiansari Ardhi Prabowo Arif Widiyatmoko, Arif Arsyadani, Fathan Aryono Adhi, Aryono Bambang Sugiantoro Budi Prasetiyo, Budi Budiman, Kholiq Cahyani, Rima Ayu Damayanti, Dela Rista Defin Andestian Dela Rista Damayanti Dimarti, Safira Chairani Edi Subkhan, Edi Fathan Arsyadani Fianti Fianti, Fianti Fitri Arum Sasi, Fitri Arum Fridiyanto Fridiyanto Hakim, M. Faris Al Hapsari, Renita Arianti Hasan Mukhibad Hasani, Azharu Alfi Ian Yulianti Indah Urwatin Wusqo Kasinyo Harto Langlang Handayani Lutfia Nur Hadiyanti, Lutfia Nur M Fadil Mardiansyah Mahargjo Hapsoro Adi Majidah Majidah Martien Herna Susanti Melati, Inaya Sari Melissa Salma Darmawan MHD. Reza M.I. Pulungan Muhamad Anbiya Nur Islam Muhamad Taufiq Mutoriq, Alfat Nila Prasetya Aryani Nor Hafizah Ngajikin Nuni Widiarti Nur Kusuma Dewi Putra, Ngurah Made Darma Rahmawati, Sinta Kurnia Renita Arianti Hapsari Rima Ayu Cahyani Ririanti, Novia Puji Shiva Maulana Khoiru Insan Sri Siswati Subagja, Mona Subhan Subhan Sugiyanto Sugiyarta Stanislaus, Sugiyarta Sujarwata Sujarwata Sukestiyarno Sukestiyarno Susilo Susilo Syuja Zhafran Rakha Krishandhie Syukron Salam, Syukron Tris Sugiarto Tsabit Azinar Ahmad Utis Sutisna Wahyu Hardyanto Wahyu Setyaningrum Widiyanto Woro Sumarni Yanuarto, Alfath YB. Praharto Yuliantika, Atika