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All Journal International Journal of Electrical and Computer Engineering Jurnal Rekayasa Proses Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Pekommas Indonesian Journal of Educational Review (IJER) Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Indonesian Journal on Computing (Indo-JC) Jurnal Inspiration JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JFMR (Journal of Fisheries and Marine Research) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) CogITo Smart Journal Insect (Informatics and Security) : Jurnal Teknik Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management Jurnal Rekayasa Proses JURNAL EDUCATION AND DEVELOPMENT MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Indonesian Journal of Applied Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CSRID (Computer Science Research and Its Development Journal) Informasi Interaktif Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Journal of Sustainable Engineering: Proceedings Series SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Teknologi Informasi dan Multimedia bit-Tech Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro Respati Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics G-Tech : Jurnal Teknologi Terapan JIKA (Jurnal Informatika) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Ilmiah Publika Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) Information System Journal (INFOS) Buletin Poltanesa Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering INFOSYS (INFORMATION SYSTEM) JOURNAL J-SAKTI (Jurnal Sains Komputer dan Informatika) Research Fair Unisri Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Teknik Informatika Journal of Comprehensive Science Jurnal Indonesia Sosial Teknologi Ceddi Journal of Information System and Technology (JST) SmartComp Teknomatika: Jurnal Informatika dan Komputer JURNAL MULTIDISIPLIN BHATARA Jurnal Pengabdian Indonesia
Arief Setyanto
Universitas Amikom Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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Klasifikasi Penyakit Daun Apel Menggunakan Arsitektur CNN dengan Transfer Learning Rahman, Aulia Tegar; Setyanto, Arief; Fatta, Hanif Al
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 6, No 1 (2024): Jurnal SENOPATI Vol 6, No 1
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2024.v6i1.6574

Abstract

Salah satu hasil produk pertanian subtropis yang dapat ditanam di Indonesia adalah apel. Dalam budidaya apel, pengendalian hama dan penyakit merupakan salah satu faktor kunci dalam perkembangan tanaman apel, karena dapat mempengaruhi hasil apel. Salah satu teknologi yang berkembang pesat dalam pendeteksian atau diagnosis penyakit tanaman dapat menyederhanakan proses klasifikasi penyakit tanaman khususnya penyakit daun apel dan membantu dalam diagnose dini adalah deep learning. Terdapat salah satu arsitektur deep learning yang dapat digunakan dalam klasifikasi citra, salah satunya Convolutional Neural Networks (CNN). Arsitektur CNN dengan transfer learning yang menghasilkan nilai akurasi yang masih bisa diterima, waktu yang diperlukan pendek pada klasifikasi penyakit daun apel. Hasil dari klasifikasi penyakit daun apel dengan VGG16 mendapatkan akurasi sebesar 99,31 %.
Analisa Prediksi Turnover Karyawan menggunakan Machine Learning Maehendrayuga, Arief; Setyanto, Arief; Kusnawi
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1999

Abstract

Penelitian ini membahas penerapan machine learning untuk memprediksi turnover karyawan, yang merupakan tantangan utama dalam manajemen Sumber Daya Manusia (SDM). Turnover karyawan sering kali disebabkan oleh berbagai faktor, termasuk ketidakseimbangan kehidupan kerja, ketidakpuasan kerja, dan minimnya peluang pengembangan karier. Dalam penelitian ini, digunakan dataset IBM HR Analytics untuk menganalisis faktor-faktor yang memengaruhi turnover karyawan. Algoritma yang diterapkan meliputi Support Vector Machine (SVM) dan Random Forest. Proses penelitian dimulai dengan pengumpulan data, eksplorasi awal, praproses data, seleksi fitur, dan penyeimbangan data menggunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Evaluasi kinerja model dilakukan menggunakan confusion matrix untuk mengukur akurasi, presisi, recall, dan f1-score. Hasil analisis menunjukkan bahwa algoritma Random Forest memberikan kinerja yang lebih baik dibandingkan SVM. Random Forest mencapai akurasi 97,72%, sedangkan SVM memperoleh akurasi 92,51%. Setelah menerapkan SMOTE, akurasi meningkat menjadi 97% untuk Random Forest dan 93% untuk SVM. Selain akurasi, Random Forest juga unggul dalam metrik presisi, recall, dan f1-score, membuktikan keandalannya dalam memprediksi turnover karyawan. Temuan ini menegaskan bahwa pendekatan machine learning dapat digunakan untuk memahami pola turnover secara lebih mendalam. Dengan prediksi yang lebih akurat, perusahaan dapat merancang strategi retensi karyawan yang lebih efektif dan berbasis data, menciptakan lingkungan kerja yang mendukung produktivitas serta meningkatkan stabilitas tenaga kerja secara keseluruhan.
Analisis Prediksi Curah Hujan Bulanan Wilayah Kota Sorong Menggunakan Metode Multiple Regression Yusuf, Muhammad; Setyanto, Arief; Aryasa, Komang
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.455

Abstract

Currently, climate change in Indonesia, which is a tropical region, is always uncertain and makes it difficult to predict weather conditions. Weather conditions can be influenced by temperature, air pressure, wind speed, humidity and rainfall. Rainfall is a climate parameter that has a high level of diversity due to climate anomalies. There are several factors that influence the characteristics of the diversity of rainfall, namely geographical, orographic, topographical, orientation and structure of the islands. These factors cause the distribution pattern of rainfall to be uneven between one area and another. For that we need a method that can solve the problem of predicting rainfall both daily, monthly and yearly. Prediction of rainfall with a statistical approach can be done through the Multiple Linear Regression method. Where in this study, rainfall is the dependent variable, while temperature and humidity are independent variables. The results obtained from the WEKA Application with a total of 60 data from 2017 to 2021, the correlation coefficient value is 0.8175, and from the evaluation results using Linear Regression, the MAE error rate is 78.8695 and the RMSE is 95.1982. It can be concluded that the effect of temperature and air on the occurrence of rainfall is 81.75%
Analisis Prediksi Curah Hujan Bulanan Wilayah Kota Sorong Menggunakan Metode Multiple Regression Yusuf, Muhammad; Setyanto, Arief; Aryasa, Komang
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.455

Abstract

Currently, climate change in Indonesia, which is a tropical region, is always uncertain and makes it difficult to predict weather conditions. Weather conditions can be influenced by temperature, air pressure, wind speed, humidity and rainfall. Rainfall is a climate parameter that has a high level of diversity due to climate anomalies. There are several factors that influence the characteristics of the diversity of rainfall, namely geographical, orographic, topographical, orientation and structure of the islands. These factors cause the distribution pattern of rainfall to be uneven between one area and another. For that we need a method that can solve the problem of predicting rainfall both daily, monthly and yearly. Prediction of rainfall with a statistical approach can be done through the Multiple Linear Regression method. Where in this study, rainfall is the dependent variable, while temperature and humidity are independent variables. The results obtained from the WEKA Application with a total of 60 data from 2017 to 2021, the correlation coefficient value is 0.8175, and from the evaluation results using Linear Regression, the MAE error rate is 78.8695 and the RMSE is 95.1982. It can be concluded that the effect of temperature and air on the occurrence of rainfall is 81.75%
Optimalisasi Akurasi Algoritma Naïve Bayes Dengan Metode Syntetic Minority Oversampling Technique (Smote) Pada Data Numerik Hizbul Izzi; Arief Setyanto; Anggit Dwi Hartanto
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.28340

Abstract

This research will classify numerical data, namely loan data taken from Kaggle. The data used amounted to 9578 datasets which included data classes with borrowers able to complete credit as many as 8045 records and loans that could not complete credit as many as 1533 records. From the amount of data there is an imbalance of classes so it is necessary to do balancing in order to get more accurate classification results. The purpose of this research is to improve the accuracy of the Naïve Bayes algorithm in classifying numerical data. Fraud in financial transactions is an example of a case of imbalanced data, where the number of legitimate transactions is much greater than those that are fraudulent. Optimizing accuracy in minority (fraud) classes is very important to avoid losses. The method used to improve the accuracy of the algorithm is the Synthetic Minority Oversampling Technique (SMOTE) by over sampling the minority of the dataset. In addition, it also uses the K-Fold Cross Validation method to evaluate the performance of the algorithm process used. Data preprocessing is done to clean the data from missing and invalid values and normalize the data so that all features are on the same scale and suitable for classification analysis. Based on the results of the analysis conducted, before the application of SMOTE the model's ability to recognize minority classes was 16.1%, while after the application of SMOTE the model's ability to recognize minority classes became 48.8%. besides that, before the application of SMOTE the model was able to predict the minority class correctly in 10 cases while after the application of SMOTE, the model was able to predict the minority class correctly in 102 cases. So it can be concluded that the SMOTE technique is able to improve the ability of the model
ESTIMATION OF BIOLOGICAL PARAMETERS OF SQUID (LOLIGO SPP) CAUGHT IN THE WATERS OF TUBAN REGENCY Setyohadi, Daduk; Kartikasari, Wahida; Setyanto, Arief; Wiadnya, Dewa Gede Raka; Harlyan, Ledhyane Eka; Sunardi, Sunardi; Nabilla, Azma Salma
Journal of Environmental Engineering and Sustainable Technology Vol 11, No 02 (2024)
Publisher : Directorate of Research and Community Service (DRPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jeest.2024.011.02.8

Abstract

Squid (Loligo spp.) belongs to the Cephalopoda group (squid, cuttlefish, octopus) and is one of the primary export commodities in the fisheries sector. National squid production increased by 5.5%, from 193,583.82 tons in 2020 to 204,156.28 tons in 2021. However, the potential sustainable catch in the Java Sea (WPPNRI 712, including the Madura Strait) has experienced an average annual decline of 1.9% from 2017 to 2022, dropping to 66,608 tons in 2022. This study aims to identify the species composition of squid, analyze length-weight relationships, and determine the mantle length at first gonad maturity (Lm). Data were obtained from fixed lift-net catches and analyzed in the Fisheries Exploitation Laboratory of Universitas Brawijaya. The results identified two main species: Photololigo duvaucelli (Indian squid) and Sepioteuthis lessoniana (bigfin reef squid). The composition of squid catches was 1.29% in purse seine operations and 2.91% in payang (seine net) operations. The length-weight relationship of both species exhibited a negative allometric growth pattern, where length growth outpaces weight gain. The sex ratio between males and females was balanced for both species. The mantle length at first gonad maturity (Lm) was greater than the mantle length at first capture (Lc), indicating that the catch was dominated by immature squid. These findings highlight the need for minimum catch size regulations to ensure the sustainability of squid resources in the Tuban waters.
Comparison of The Performance of SVR, KNN and Decision Tree Methods in Predicting Rice Production Hamdikatama, Bimantyoso; Kusrini, Kusrini; Setyanto, Arief
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.10133

Abstract

Rice holds importance in Indonesia as a commodity driving the economy and improving societal well-being, however, its production encounters obstacles attributed to the effects of drastic climate variations. This study sought to evaluate how Support Vector Regression (SVR) k Nearest Neighbors (KNN) and Decision Tree models perform in forecasting rice yields while considering variables related to climate change. The research process included stages such, as gathering and cleaning the information before exploring and analyzing it to apply metrics and implement algorithms like Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) and R² Score, for evaluation purposes. The findings obtained from the study indicate that the Decision Tree technique is efficient, achieving a minimal deviation rate of 0%. This outcome implies that the model effectively grasped the core patterns within the dataset while reducing errors effectively. The KNN model displayed performance levels and suggested room, for enhancement with parameter adjustments; however, SVM Regression was deemed fitting for the datasets needs. The results emphasize the significance of choosing the algorithm for modeling in agriculture and stress the necessity, for additional research to confirm these findings in various datasets.
Kladistik Genera Famili Leiognathidae melalui Penelusuran Morfologi Eksternal dan Otolith: Cladistic Genera of Family Leiognathidae Based on External Morphology and Otolith Samuel, Pratama Diffi; Wiadnya, Dewa Gede Raka; Anam, M. Choirul; Setyanto, Arief; Khamidah, Nur; Yasmin, Delviega Aisyah; Astuti , Septiana Sri
JFMR (Journal of Fisheries and Marine Research) Vol. 9 No. 1 (2025): JFMR on March
Publisher : Faculty of Fisheries and Marine Science, Brawijaya University, Malang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jfmr.2025.009.01.15

Abstract

Anggota famili Leiognathidae atau Peperek termasuk dalam kategori minor commercial, berfungsi sebagai komoditas ketahanan pangan sehingga kurang mendapat perhatian untuk diteliti. Penelitian ini bertujuan untuk membuktikan hipotesis penemuan seluruh genera dari Leiognathidae pada perairan Pantai Jawa Timur. Sampel ikan dikoleksi dari hasil tangkapan nelayan dengan alat penangkapan ikan; Jaring Tarik, Cantrang, dan Mini-Trawl dari Januari 2023 sampai Oktober 2024. Analisis genus dilakukan melalui deskripsi morfologi eksternal, morfometri, dan penyelidikan otolith. Studi otolith dilakukan melalui koleksi sagittae dari tulang telinga di belakang otak. Analisis morfometri untuk memperjelas definisi bentuk tubuh menggunakan perangkat lunak TpsDig. Total 12 variabel morfologi digunakan untuk menjelaskan masing-masing kerabat pada genus. Sementara deskripsi otolith dianalisis dengan menggunakan 15 variabel bentuk, cekungan, dan tonjolan dari otolith. Dendogram dihasilkan dari analisis morfologi dan otolith untuk memisahkan kekerabatan di antara genus. Hasil analisis membuktikan bahwa terdeskripsi total 10 genera dari famili Leiognathidae yaitu; Leiognathus, Aurigequula, Eubleekeria, Photopectoralis, Nuchequula, Karalla, Gazza, Deveximentum, Equulites, dan Photolateralis. Genus Gazza ditemukan pada seluruh lokasi sampling. Namun genus Karalla hanya ditemukan pada lokasi sampling di Selatan Barat Jawa Timur (Pantai Dangkal Pacitan, dan Prigi Trenggalek). Hasil analisis dendogram berhasil menempatkan Equulites satu kerabat dengan Photolateralis, namun tidak berhasil memisahkan antara Leiognathinae dengan Gazzinae. Sebaliknya, analisis menggunakan morfologi otolith tidak berhasil menempatkan Equulites satu kelompok dengan Photolateralis, namun bisa memisahkan antara sub famili Leiognathinae dengan Gazzinae. Kondisi lingkungan geografis mungkin menjadi faktor utama terjadinya adaptasi morfologi eksternal dan otolith yang berbeda. Deskripsi morfologi dan otolith bisa digunakan sebagai indikator apomorfi genus. Analisis genetik melalui DNA barcoding masih diperlukan untuk menelusuri kekerabatan diantara genus.   Members of family Leiognathidae are included in the minor commercial category, functioning as a food security commodity so that they have received less attention for research. The study aims to prove the hypothesis of the discovery of all genera of Leiognathidae within coastal waters of East Java. Fish samples were collected from the catches of fishermen using fishing gear; Beach Seine, modified Danish Seine, and Mini-Trawl, from January 2023 to October 2024. Genera analysis was carried out through external morphological descriptions, morphometry, and otolith investigations. Otoliths were collection of sagittae from the ear bones behind the brain. Morphometric analysis to clarify the definition of body shape were using TpsDig software. A total of 12 morphological variables were used to describe each genus within family. While the otolith description was analyzed using 15 variables of shape, depression, and protrusion of the otolith. Each dendrogram was generated from morphological and otolith analysis to separate the clade among genera. The results of the analysis proved that all 10 genera of Leiognathidae were described, consisting of: Leiognathus, Aurigequula, Eubleekeria, Photopectoralis, Nuchequula, Karalla, Gazza, Deveximentum, Equulites, and Photolateralis. The genus Gazza was found in all sampling locations. However, the genus Karalla was only described in two sampling locations in Southwest of East Java (Pantai Dangkal Pacitan, and Prigi Trenggalek). The results of dendogram analysis succeeded in placing Equulites in the same clade as Photolateralis, but failed to separate Leiognathinae from Gazzinae. On the other hand, the analysis using otolith morphology failed to place Equulites in the same group as Photolateralis, but could separate Leiognathinae from Gazzinae. Geographical barriers and environmental factors might be the main factor in the occurrence of different morphological and otolith adaptations. Genera can be distinguished through external morphology and otolith description. Genetic analysis through DNA barcoding is still needed to trace the lineage among genera of Leiognathidae.
FOREST FIRE LOCATION AND TIME RECOGNITION IN SOCIAL MEDIA TEXT USING XLM-ROBERTA Hafidz Sanjaya; Kusrini Kusrini; Kumara Ari Yuana; Arief Setyanto; I Made Artha Agastya; Simone Martin Marotta; José Ramón Martínez Salio
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6194

Abstract

Forest fires have become a serious global threat, significantly impacting ecosystems, communities, and economies. Although remote sensing technology shows potential, limitations such as time delays, limited sensor coverage, and low resolution reduce its effectiveness for real-time forest fire detection. Additionally, social media can serve as a multimodal sensor, presenting multilingual text data with rapid and global coverage. However, it may encounter challenges in obtaining location and time information on forest fires due to limitations in datasets and model generalization. This study aims to develop a multilingual named entity recognition (NER) model to identify location and time entities of forest fires in social media texts such as tweets. Utilizing a transfer learning approach with the XLM-RoBERTa architecture, fine-tuning was performed using the general-purpose Nergrit corpus dataset containing 19 entities, which were relabeled into 3 main entities to detect location, date, and time entities from tweets. This approach significantly improves the model's ability to generalize to disaster domains across multiple languages and noisy social media texts. With a fine-tuning accuracy of 98.58% and a maximum validation accuracy of 96.50%, the model offers a novel capability for disaster management agencies to detect forest fires in a scalable, globally inclusive manner, enhancing disaster response and mitigation efforts.
Evaluating Transformer Models for Social Media Text-Based Personality Profiling Hartanto, Anggit; Ema Utami; Arief Setyanto; Kusrini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6157

Abstract

This research aims to evaluate the performance of various Transformer models in social media-based classification tasks, specifically focusing on applications in personality profiling. With the growing interest in leveraging social media as a data source for understanding individual personality traits, selecting an appropriate model becomes crucial for enhancing accuracy and efficiency in large-scale data processing. Accurate personality profiling can provide valuable insights for applications in psychology, marketing, and personalized recommendations. In this context, models such as BERT, RoBERTa, DistilBERT, TinyBERT, MobileBERT, and ALBERT are utilized in this study to understand their performance differences under varying configurations and dataset conditions, assessing their suitability for nuanced personality profiling tasks. The research methodology involves four experimental scenarios with a structured process that includes data acquisition, preprocessing, tokenization, model fine-tuning, and evaluation. In Scenarios 1 and 2, a full dataset of 9,920 data points was used with standard fine-tuning parameters for all models. In contrast, ALBERT in Scenario 2 was optimized using customized batch size, learning rate, and weight decay. Scenarios 3 and 4 used 30% of the total dataset, with additional adjustments for ALBERT to examine its performance under specific conditions. Each scenario is designed to test model robustness against variations in parameters and dataset size. The experimental results underscore the importance of tailoring fine-tuning parameters to optimize model performance, particularly for parameter-efficient models like ALBERT. ALBERT and MobileBERT demonstrated strong performance across conditions, excelling in scenarios requiring accuracy and efficiency. BERT proved to be a robust and reliable choice, maintaining high performance even with reduced data, while RoBERTa and DistilBERT may require further adjustments to adapt to data-limited conditions. Although efficient, TinyBERT may fall short on tasks demanding high accuracy due to its limited representational capacity. Selecting the right model requires balancing computational efficiency, task-specific requirements, and data complexity.
Co-Authors (Menunda Publikasi) Abdillah, M A Agastya, I Made Artha Agung Budi Prastyo Agung, Kris Agus Sukarno Agus Tumulyadi Agustina Rahmawati Ahmad Afief Amrullah Ahmad Afief Amrullah Ahmad Naufal Labiib Nabhaan Ahmad Tantoni Ainul Yaqin Al Maky, Nuril Huda Aliyah, Nada Rahma Amanda Rifan Fathoni Amir Fatah Sofyan Amiruddin Khairul Huda Ammara, Laya Amrullah, Ahmad Afief Anam, M. Choirul Anang Anang Andi Kriswantono Andik Isdianto Anggit Dwi Hartanto Anggit Hartanto Annisa Gatri Zakinah annisa gatri zakinah Anthon Andrimida, Anthon Anton, Tri Arbiansyah, Moh Junit Ariefandi, Muhammad Fikri Asadi, M. Arif Askar, Muhammad Ichfan Asmirijal, Amrey Syahnur Asro Nasiri Asro Nasiri Asro Nasiri Astika Wulansari Astuti , Septiana Sri Atmaja, Albertus Aldo Danar Atminenggar, Alinda Najma Aulia Lanudia Fathah Basit, Muhammad Abdul Bawan, Sarah Bunda Desi Béjar, Rodrigo Martínez Berlania Mahardika Putri Constantin Menteng Daduk Setyohadi Darmawan Ockto Sutjipto Dedi Tri Hermanto Dewa Gede Raka Wiadnya Dewa Gede Raka Wiadnya Dewa Gede Raka Wiadnya, Dewa Gede DHANI ARIATMANTO Dhea, Luthfia Ayu Dhiana Puspitawati Diah, M. Dian Rusvinasari Dinar Mustofa Dwi Satrio Anurogo Eko Pramono Eko Pramono Eko Pramono Ema Utami Emha Emha Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi F Purwanto Fadjeri, Akhmad Fathah, Aulia Lanudia Fazlul Rahman Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Fiqih Akbari Gatut Bintoro Gibran, Ibrahim El Gibran, Khalil Ginting, Meliani Ananda Br. Gunawan Wicahyono Hadin La Ariandi Hadiyah, Lisa Nur Hafidz Sanjaya, Hafidz Hamdallah, Dika Puja Hamdikatama, Bimantyoso Hamka Suyuti Hamzah Hamzah HANIF AL FATTA Hanif Al Fatta Hanif Al Fatta Hanif Al Fattah Hanifa Ramadhani Hari Susanto Harlyan, Ledhyane Eka Henderi . Hendi Muhammad, Alva Heri Sismoro Hidayat, Aji Said Wahyudi Hidayat, Kardilah Rohmat Hizbul Izzi I Made Adi Purwantara I Made Artha Agastya Ilham Mubarog Imam Syafii Imam Syafii Imam Thoib Irianies Cahya Gozali Irwan Jatmiko Ishaq, Syafrial Yanuar Jimmy H Moedjahedy José Ramón Martínez Salio Kamila, Firda Nikmatul Karaman, Jamilah Kartikasari, Wahida Khairan marzuki Khasanah, Nabiila Rizqi Kholida Zia Abidin Komang Aryasa Kris Agung Kudrati, Amelinda Vivian Kumara Ari Yuana Kumoro, Danang Tejo Kurniawan, Mei P Kusnawi Kusnawi KUSRINI Kusrini Kusrini Kusrini, Kusrini López, Alba Puelles M. Diah M. RUDYANTO ARIEF M. Rudyanto Arief Maehendrayuga, Arief Mardya Hayati Marsela, Kristina Martiani, Evi Martínez-Béjar, Rodrigo Mei P Kurniawan Mei P. Kurniawan Miftahul Madani Mohamad Syafri Lamato Morita Puspita Sari Muchamad Zainul Muhamad Maksum Hidayat Muhammad Arif Asadi, Muhammad Arif Muhammad Arif Rahman Muhammad Azmi Muhammad Ghozaly Salim Muhammad Javier Irsyad Muhammad Reza Muhammad Reza Riansyah Muhammad Yusuf Munandar, Arief Muqorobin Muqorobin Nabhaan, Ahmad Naufal Labiib Nabilla, Azma Salma Nadea Cipta Laksmita Nasiri, Asro Naufal Hilda Bahtiar nfn Sarip Nggego, Dedy Abdianto Ni Nyoman Utami Januhari, Ni Nyoman Nico Rahman Caesar Nila Feby Puspitasari, Nila Feby Nina Kurnia Hikmawati Nisrina, Aliyya Nizery, Sefhanissa Puspa Retno Nuddin Harahab Nugroho, Agung Nur Khamidah oktiyas muzaky Luthfi, oktiyas muzaky Pahlawan, Muammar Reza Pangestu, Wanda Suryani Pattisahusiwa, Annisa Shafira P. Prayoghi, M. Lukman Publikasi), (Menunda Putra, Muhammad Naufal Eka Putri, Berlania Mahardika Rachmanto, Rakandhiya Daanii Rafif Zul Fahmi Rahmad Arif Setiawan Rahman, Aulia Tegar Rahmat Taufik R.L Bau Rakandhiya Daanii Rachmanto Ramdhani, Mohamad Dhicy Rarasrum Dyah Kasitowati Ratno Kustiawan Ria Andriani Ripto Sudiyarno Rismayani Rismayani Roni Sasongko Rudyanto Arief Sadikin, Moh. Fal Samuel, Pratama Diffi San Sudirman Saputra, Tedy Eko Sarah Bunda Desi Bawan Sarip, nfn Seniwati, Erni Septiansyah, Moch. Rafli Shahruri, Rifandi Annas Simone Martin Marotta Siswo Utomo, Mardi Siti Alvi Sholikhatin Siti Halimah Soejono, Ajie Wibowo Sriyati Sriyati Stephan Adriansyah Hulukati Suardi, Heri Sucianingsih, Ni Komang Diah Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudirman, San Suhardi Aras Sukoco Sunardi Sunardi Supriyadi Supriyadi Supriyadi Supriyadi Suwanto Raharjo Suyadi Suyadi Suyuti, Hamka Syarief, Salsabila Nazmie Putri TONNY HIDAYAT Totok Wahyu Caturiyanto Tri Djoko Lelono Tumulyadi, Agus Tyas, Herlin Widi Aning Utama, Andria Ansri Veithzal Rivai Zainal Wahyu Nugroho Widhiarta, Widhiarta Wijaya, Sony Yasmin, Delviega Aisyah Yeni Kartika Sari, Yeni Kartika Yorarizka, Putri Devi Yuliana Yuliana Yumna, Orryza Nayla Zul Hisyam