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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) snimed Jurnal Ilmiah Farmasi Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) PREPOTIF : Jurnal Kesehatan Masyarakat JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal Research of Social Science, Economics, and Management Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Fudholi, Dhomas Hatta
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Aspect-Based Sentiment Analysis Pada Aplikasi Pelacakan Kasus Covid-19 (Studi Kasus: Pedulilindungi) Suryatin, Suryatin; Fudholi, Dhomas Hatta; Dewa, Chandra Kusuma; Iman, Nur
Jurnal Sistem Informasi dan Sistem Komputer Vol 9 No 1 (2024): Vol 9 No 1 - 2024
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v9i1.304

Abstract

Berbagi pengalaman melalui internet dan media sosial dapat menunjukan sikap dan perasaan dalam bentuk umpan balik. Aplikasi publik yang banyak disoroti pada masa wabah corona virus yaitu aplikasi pedulilindungi yang merupakan aplikasi monitoring perkembangan Corona Virus Disease di Indonesia. Salah satu fenomena timbulnya Aspect-based sentiment dalam pada prilaku sentimentil masyarakat terhadap layanan aplikasi pedulilindungi. Penelitian ini bertujuan untuk mengetahui besarnya nilai sentimen pada layanan pedulilindungi dan berfokus pada aspect based sentiment analysis (ABSA) pada domain ulasan aplikasi pemerintah. Analisis terdiri dari user interface, user experience, fungsionalitas dan work scurity. Metode yang digunakan meliputi klasifikasi sentimen dan aspek dengan metode deep learning (CNN,GRU dan TCN). Data primer bersumber dari hasil ulasan aplikasi pedulilindungi dengan teknik scraping pada situs https://www.pedulilindungi.id/. Hasil penelitian menunjukan bahwa terdapat enam aspek klasisifikasi sentimen pada aplikasi pedulilindungi yaitu aplikasi, user interface, user experience, kode OTP, cek sertifikat vaksin, bukti akses layanan. Hasil penelitian juga menunjukan bahwa metode CNN memperoleh nilai skor akurasi terbaik pada klasifikasi sentimen sebesar 98% dan klasifikasi aspek sebesar 97%.
Leveraging BiLSTM and LDA for Analyzing and Dashboarding User Feedback in Applications Mutmainah, Siti; Fudholi, Dhomas Hatta
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7022

Abstract

The idea of prioritizing customer satisfaction to uphold or improve the excellence of a product or service can incorporate the utilization of user feedback. However, to provide a comprehensive visual summary to application developers or stakeholders, it is important to provide a detailed description of user sentiment issues. In this study, the data source used is user feedback from local telemedicine applications in Indonesia. This research builds a framework of deep learning to perform user feedback analysis and applies topic modeling to sentiment clusters. then builds visual construction of research results effectively and efficiently, to facilitate stakeholders in making decisions. Build a framework to analyze user feedback utilizing deep learning BiLSTM + IndoBERT for sentiment classification and LDA to model topics in sentiment groups. The results show that most of the user reviews of the five telemedicine applications have a positive sentiment at 91%. The model used has good prediction performance with the accuracy of the BiLSTM model with IndoBERT 96.44%. The negative sentiment group comprises 12 topics (0.58446), the most significant topics being 35.4% about telephone broadcasting, 25.3% payments, and 8.5% about medicine purchase service. For the imbalanced data case, BiLSTM showed good accuracy and precision values. The classifications and topics generated by deep learning models are affected by improper data labeling, so it is necessary to explore the data labels generated.
Klasifikasi Emosi Pada Data Text Bahasa Indonesia Menggunakan Algoritma BERT, RoBERTa, dan Distil-BERT Basbeth, Faishal; Fudholi, Dhomas Hatta
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7472

Abstract

Previous studies in the context of sentiment analysis have been conducted in various types of text domains, including product reviews, movies, news, and opinions. Sentiment analysis focuses on recognizing the valence of positive or negative orientations. In sentiment analysis, something that is less explored but is needed in analysis is the recognition of types of emotions / classification of emotions. This research has contributed to knowledge of the applicationof distilBERT to Indonesian Language data which is still relatively new and the success of fine-tuning and hyperparameter-tuning that has been applied to distilBERT with various comparison methods. Emotion classification can help provide the right decisions in a company, government, and marketing strategy. Emotion classification is part of sentiment analysis which has more detailed and in-depth emotion labels. From these various urgencies, this research builds and fine-tuning distilBERT model to help classify emotions from an Indonesian sentence. Emotion classification in Indonesian language data using the distilBERT model is still relatively new. This research consists of 4 steps: data collection, data preprocessing, hyperparameter-tuning and fine-tuning, and comparison of results from various models that have been applied in this research: BERT, RoBERTa, and distilBERT. DistilBERT got the highest training accuracy = 94.76, the highest testing accuracy was obtained by distilBERT-frezee = 86.67, and the highest f1-score was obtained by distilBERT Modif = 87. In distilBERT-frezee the main cause of the model getting significant results is the dropout hyperparameter which reduces the f1-score value by 3 if it is not used, and the second cause is the freeze-layer which reduces the f1-score value by 1 if it is not used.
Pengembangan Sistem Deteksi On-Shelf Availability Produk Menggunakan Algoritma YOLOV8 pada Aplikasi Beregerak Andaru, Gabriel Imam; Fudholi, Dhomas Hatta
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 2 (2024): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i2.767

Abstract

Research related to retail operations has been a major focus in recent years, driven by rapidly changing market dynamics and the importance of product availability on store shelves to meet customer satisfaction. The concept of On Shelf Availability (OSA) has become key in ensuring products are available when needed. However, the challenge in retail management lies in monitoring thousands of different products, which is time-consuming and resource-intensive. To address this issue, an efficient object detection solution is needed. The research implements the YOLOv8 algorithm in detecting out-of-stock items, particularly in the context of mobile devices with resource limitations. In order to achieve this goal, the research adopts a comprehensive methodology, starting from direct data collection from supermarkets, data processing, labeling, to model training using transfer learning techniques. Transfer learning method is chosen to overcome data limitations and accelerate the model training process, enabling faster adaptation to object detection conditions at specific locations. Test results show that YOLOv8s delivers the best performance with an accuracy of up to 94.7%, allowing real-time object detection. Testing is conducted on various mobile devices, including Samsung A54 and Samsung A6, where YOLOv8n consistently performs with an inference time of 41.46 ms on Samsung A54 and 257.73 ms on Samsung A6. The main contribution of this research is to enhance object detection capabilities on devices with low computational power, such as mobile devices, and provide an effective solution to the problem of product availability on store shelves. Thus, this research not only brings positive impact to retail management but also drives the development of object detection technology in the context of resource-limited devices and unstable internet connections.
SCOOPING REVIEW SISTEM INFORMASI MANAJEMEN INVENTORI PERBEKALAN KESEHATAN MENGGUNAKAN WEB/ DATA BASE Handayani, Eka; Saefudin, Saefudin; Fudholi, Dhomas Hatta
PREPOTIF : JURNAL KESEHATAN MASYARAKAT Vol. 8 No. 3 (2024): DESEMBER 2024
Publisher : Universitas Pahlawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/prepotif.v8i3.32793

Abstract

Sistem Informasi Manajemen Inventori Perbekalan Kesehatan memainkan peran penting dalam pengelolaan obat, bahan medis habis pakai (BMHP), dan perbekalan kesehatan lainnya di gudang farmasi. Efektivitas pelayanan kesehatan sangat bergantung pada ketersediaan dan akurasi inventori, namun banyak gudang farmasi masih menghadapi tantangan dalam pengelolaan persediaan karena proses pencatatan yang manual. Oleh karena itu, diperlukan solusi berbasis web atau basis data untuk meningkatkan efisiensi dan akurasi pengelolaan inventori. Studi ini bertujuan untuk mengkaji literatur terkait Sistem Informasi Manajemen Inventori Perbekalan Kesehatan di Gudang Farmasi, khususnya yang menggunakan teknologi berbasis web atau basis data. Kajian ini juga bertujuan untuk mengidentifikasi tren, manfaat, dan tantangan yang dihadapi dalam penerapan sistem tersebut di fasilitas kesehatan. Scoping review ini dilakukan melalui tiga tahap: identifikasi tujuan penelitian, pencarian literatur dari basis data Google Scholar menggunakan kata kunci yang relevan, serta analisis dan penyusunan laporan. Dari pencarian literatur, diperoleh 20 artikel yang membahas sistem manajemen inventori di gudang farmasi dan puskesmas. Sebagian besar (60%) gudang farmasi masih menggunakan sistem manual untuk pencatatan dan pelaporan inventori, sedangkan 40% lainnya telah mengembangkan atau sedang menguji coba sistem berbasis web atau basis data. Studi menunjukkan bahwa sistem digital memiliki keunggulan dalam meningkatkan efisiensi operasional, akurasi data, dan keamanan informasi dibandingkan dengan sistem manual. Penggunaan sistem informasi manajemen inventori berbasis digital di gudang farmasi memberikan banyak manfaat, termasuk penghematan waktu, peningkatan aksesibilitas, dan keamanan data yang lebih baik.
Stability and beyond-use date of anesthetic agents used in surgical procedures: a review: Stability and Beyond-Use Data of Anesthesia Wijayanti, Shofia; Hanifah, Suci; Fudholi, Dhomas Hatta
Jurnal Ilmiah Farmasi Vol. 20 No. 2 (2024): Jurnal Ilmiah Farmasi
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jif.vol20.iss2.art4

Abstract

Background: Anaesthesia drugs are often divided into other syringes to be soluted or mixed with other medications to share with other patients for the sake of efficiency. Objectives: This study aims to know the stability of anesthetic agents and the compatibility with co-simultaneous drugs used. Methods: This review was conducted by searching literature through the following databases: PubMed, Science Direct, and Google Scholar. The keywords used in the search for articles were "stability," "beyond use date," "anesthetic drug," and "intravenous."Results: The data showed that mixing fentanyl with levo-bupivakain or epinefrin is relatively stable up to one month, but it decreases only for 72 hours in dextrose 5% or normal saline (NS). Pethidin can be mixed with acetaminophen and metoclopramide using dextrose, NS, or water and stored up to 24 hours. Midazolam diluted in dextrose 5% (D5) or mixed with other medications maintains stability for up to 14 days or more. Stability of ketamine is 24 hours longer whether it is mixed in solvent or acetaminophen. Mixing with propofol induces instability because of the emulsion form of propofol. Conclusion: In general, the anesthetic drugs of fentanyl, pethidine, midazolam, and ketamine are stable and safe for preparation and administration in more than 24 hours. These four medications are compatible with NS and D5 and all tested medications during 24 hours.
Deteksi Indikasi Kelelahan Menggunakan Deep Learning Fudholi, Dhomas Hatta; Nayoan, Royan Abida N; Suyuti, Maghfirah; Rahmadi, Ridho
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.292

Abstract

Many students experience fatigue due to lack of sleep which can be caused by a psychological conditions or bad habits. Lack of sleep can affect student’s performance academically and causes many illnesses, stress and depression. Students with fatigue causes students to not study well, increasing risk of academic failure and will lead to having low GPA. In this research, fatigue detection is carried out to find out which students are experiencing fatigue. In this study, an annotated video dataset was used with a total of 18 subjects acted drowsy and alert. Fatigue detection is based on mouth movements, therefore mouth annotation is used. Mouth annotation has 2 categories, namely annotation 0 which indicates a closed mouth and annotation 1 which indicates the mouth is yawning. Previous study proves ResNet50 has better performance than other pre-trained models such as AlexNet, Clarifia, VGG-16, and GoogLeNet-19. We also applied image augmentation which is useful for providing new image variations to the model in each epoch by changing the rotation, random shift, and random zoom. ResNet50 model is used to perform binary classification which has two outputs, namely mouth stillness and yawning. The results of the frame classification are evaluated using precision, recall and f1-score. By using ResNet model, the results of the classification of frames labeled 0 or mouth stillness, obtained a precision of 0.72, a recall of 0.88, and an f1-score of 0.79. Meanwhile, the frame classification labeled 1 or yawning has a precision value of 0.85, a recall of 0.65, and an f1-score of 0.74.
Deteksi Cyberbullying berdasarkan Unsur Perbuatan Pidana yang Dilanggar dengan Naive Bayes dan Support Vector Machine Manoppo, Tommy Nugraha; Fudholi, Dhomas Hatta
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.293

Abstract

Lack of understanding by Indonesian social media user about law impact inflicted to cyberbullying perpetrators makes many cyberbullying cases has not handled properly and ended up with nothing. Indonesia hasn’t yet law authority that govern cyberbullying in specific, causing no guideline regard the definition about cyberbullying itself. There is an extension about definition of violence which state that violence is not only physically deliver, but also psychologically, referred an inferences cyberbullying characteristics possibly qualify in element of criminal act. Therefore, the element of criminal act can be used as a basis for detecting potential of cyberbullying. In this research, literature review is used to determine the elements of criminal acts related to the characteristics of cyberbullying and also in finding a model classifier to detect cyberbullying messages. So there are 5 criminal acts related to cyberbullying characteristic which insult, accuse with defamation, hatred about ethnicity, religion, race and inter-group relations, threat of violence, and threat of telling secret. Total of 5000 tweets are collected as a dataset. Feature extraction, using the N-gram method with TF-IDF weighting is expected to obtain sentiment based on the use of words. The context of language becomes important in this study, so the dataset annotation process is carried out by linguist. The results on the application of the two model classfier were Naïve Bayes and SVM after applying resampling by over-sampling using SMOTE method, can correctly predict the potential for cyberbullying by their violated element of criminal act with the average performance measurement of 90%.
Model Identifikasi Penyakit Pada Tumbuhan Padi Berbasiskan DenseNet Pailus, Muhammad; Fudholi, Dhomas Hatta; Hidayat, Syarif
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.478

Abstract

Errors in identifying diseases in rice plants can cause the potential for crop failure to increase by 18-80%, according to data from the Indonesian Ministry of Agriculture. This could be due to the lack of expertise in agriculture when compared to the amount of land in Indonesia. Recent research in the field of deep learning using neural networks has achieved remarkable improvements. Research on the identification of plant diseases in rice plants, using the MobileNet, NasNet and SqueezeNet architecture that supports mobile devices has been carried out. The experimental results show that the proposed architecture can achieve an accuracy of 93.3%. Motivated by previous research, this research will use DenseNet architecture (Dense Convolutional Network) to detect diseases in rice plants. The dataset used is relatively small, between 100-200 photos for each disease. To cover the lack of dataset augmentation is done to the dataset. The final results obtained are quite satisfactory with an accuracy of 96% with a Weighted Average of 97%.
Story Generator Bahasa Indonesia dengan Skip-Thoughts Mustofa, M; Fudholi, Dhomas Hatta
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.479

Abstract

Currently, there are many studies that want computers to be able to imitate human creativity in stringing words into writing like a writer. This study aims to use the RNN algorithm to produce automatic story writing in Indonesian. The main contribution in this research is the creation and evaluation of the RNN algorithm based on the skip-thoughts model using an Indonesian language dataset. The skip-thoughts model consists of an encoder in the form of single GRU layer with 500 hidden units, and two decoders with single GRU layer each with 500 hidden units. The function of the encoder is to do the word mapping process from the input sentence, while the decoder predicts the sentence before (previous decoder) and the sentence after (next decoder) from the input sentence. The dataset used in the model training is in the form of stories in Indonesian with the genres of folklore and short stories. The model training process is run in 100 epochs, using the ADAM optimizer to get the optimal model. Based on the results of the assessment of respondents who have a background as writers, the folklore model shows a fairly good rating (average score of 65) for the S-P-O-K criteria, and a low rating for criteria of linkage between sentences (average score of 38) and the context of the whole story (average score of 32). The short story of life model shows a good rating (average score of 73) for the S-P-O-K criteria, and a low rating for the linkage between sentences criteria (average score of 48), and the context of the whole story (average score of 42). Based on the results of the assessment, the skip-thoughts model used in the Indonesian story generator has worked well, but it can still be improved by increasing the number of training datasets for each story genre used, as well as being more specific in determining the genre in order to obtain story integrity better.
Co-Authors Ahmad Fathan Hidayatullah, Ahmad Fathan Al-Faruq, Umar Abdul Aziz Al-Sabahi, Abdullah Yahya Moqbel Alhanafi, Ahmad Azzam Andaru, Gabriel Imam Arifin, Dimastyo Muhaimin Arrie Kurniawardhani Arrochman, Ibnu Fajar Avilba, Pandu Bagus W, Alan Tusa Basbeth, Faishal Bauravindah, Achmad Chairullah Naury Dewa, Chandra Kusuma Eka Handayani Eko Prasetio Widhi Fahmi, Yunizar Fikri, Khairul Kurniawardhan, Arrie Lizda Iswari Manoppo, Tommy Nugraha Mustofa, M Najmudin, Nabil Naury, Chairullah Nayoan, Royan Abida N Nayoan, Royan Abida N. Nur Iman Pailus, Muhammad Ridho Rahmadi Rima Tri Wahyuningrum Saefudin Saefudin Sahria, Yoga Satyatama, Mochamad Rezky Septia Rani Siti Mutmainah Suci Hanifah Suryatin, Suryatin Susanto, Prastyo Eko Suyuti, Maghfirah Syarif Hidayat Wijayanti, Shofia Yasmin, Saarah Muthiah Yudi prayudi Yurio Windiatmoko, Yurio Zahra, Annisa