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All Journal Jurnal Simetris JOIV : International Journal on Informatics Visualization Emerging Science Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi MURA Journal of Information Systems and Informatics APTIKOM Journal on Computer Science and Information Technologies (CSIT) IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences Jurnal Bina Komputer Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi Mura
Kurniawan, Tri Basuki
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Social Sciences Other

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IDENTIFICATION WORD SPACING OF ERRONEOUS SENTENCES ON INDONESIAN SCIENTIFIC Ariana, Sunda; Syahputra, Hadi; Kurniawan, Tri Basuki
APTIKOM Journal on Computer Science and Information Technologies Vol 5 No 1 March (2020): APTIKOM Journal on Computer Science and Information Technologies (CSIT)
Publisher : APTIKOM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/csit.v5i1 March.133

Abstract

Scientific writings in Indonesian language  must the rules of the General Guide to the Reformed Indonesian Spelling especially in terms of word spacing. good word spacing is proportional to the width of the typeface. generally use the align right facility in order to have article look neat in typing, but causes space from word to word over space. Additionally, according to the General Guide book for the updated Indonesian spelling, there are rules that use the distance of words with punctuation after and before punctuation. This research is intended to make for detecting applications using word spacing of the Regular Expression String algorithm and to comment on articles based on principles that are in accordance with the General Guidelines for Indonesian Reformed Spelling.
Pengenalan Pola Angka Menggunakan Pendekatan Optimisasi Sistem Kekebalan Buatan (Artificial Immune System) Prahartiningsyah, Anggari Ayu; Kurniawan, Tri Basuki
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.2997

Abstract

The general election in Indonesia itself still experiences technical and non-technical problems where the technical problems occur in the recapitulation of votes from sheet C1 which are still incorrectly inputted and done manually. The problem occurred with the difference in the uploaded C1 data and the data in the KPU Situng and the C1 sheet uploaded was blurry, unclear, sheet C1 which was crossed out or folded in the KPU Situng. The purpose of this research is to reduce errors in data input and change the work that is done manually to the system, create a number pattern recognition system using an Artificial Immune System optimization approach, test and analyze the work of the system by taking into account the level of accuracy, preciseness and speed in recognize number patterns. The system created to applies an artificial immune system optimization approach with the Artificial Immune System using the Randomized Real-Valued Negative Selection Algorithm algorithm.
Pengembangan Model Untuk Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes Qisthiano, M Riski; Kurniawan, Tri Basuki; Negara, Edi Surya; Akbar, Muhammad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3030

Abstract

Many parameters affect the timeliness of student graduation, starting from the student's interest in certain majors, the type of class chosen, to the grades for each semester obtained. This is a determining factor in how students can graduate on time or not at the end of their education. So a model is needed to predict student graduation rates on time, using alumni data whose data is obtained from several universities in Palembang City. The model used is a Naïve Bayes algorithm which serves as a model for classification. The dataset used is alumni data that has been collected from several universities, while the attributes used are the Department, College, Class Type, Temporary IP Value from semester 1 to 4, graduation year, and college generation. Then from the attributes and models used, the researcher used the Python 3 programming language and the Jupyter Notebook tools to process the prepared dataset. Furthermore, the distribution of the dataset is divided by 70% for training data and 30% for testing data. To test the algorithmic process used by researchers using K-Fold Validation. The results of this study are the accuracy of the prediction model carried out, where the accuracy results obtained from the Python 3 programming language and the Naïve Bayes algorithm are 0.8103.
Metode Klasifikasi Gejala Penyakit Coronavirus Disease 19 (COVID-19) Menggunakan Algoritma Neural Network Rahmi, Rahmi; Antoni, Darius; Syaputra, Hadi; Fatoni, Fatoni; Kurniawan, Tri Basuki
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1406

Abstract

Coronavirus Disease 19 (COVID-19) adalah virus baru yang dapat menyebabkan infeksi saluran pernafasan. Virus ini  berasal dari hewan yang dapat menular ke manusia melalui percikan ludahnya. Menurt data epidemiologis, rata-rata penderita virus ini berusia 15-80 tahun. Virus ini memiliki masa inkubasi 3-14 hari yang memiliki gejala awal yaitu demam tinggi, sesak napas, batuk dan pilek. Indonesia mwmiliki 2 kasus pertama pada 2 Maret 2020, Covid-19 meningkat secara teratur pada 29 Desember 2020 data menunjukkan 719.219 ribu orang dipastikan terjangkit Covid-19. Masalah yang diangkat dalam penelitian ini adalah bagaimana mengklasifikasikan risiko tertular virus Covid-19 dari gejala yang ditimbulkan. Tujuan dari penelitian ini adalah untuk mengetahui nilai akurasi dari klasifikasi resiko tertular virus Covid-19 berdasarkan instrument yang digunkan dari metode Cross Industry Standard Process for Data Mining (CRISP-DM). Dataset yang digunakan peniliti diambil dari website http://github.com/nshomron/covidpred. Penelitian ini menggunakan Algoritma Neural Network (NN) dengan bantuan alat Phyton, akurasi Algoritma Neural Ntwork (NN) diperoleh nilai sebesar 95%, artinya telah menunjukkan hasil klasifikasi yang baik. Peneliti juga menguji dengan Algoritma Logistic Regression namun nilai akurasi yang diperoleh tidak jauh berbeda dengan Algoritma NN, Algoritma Logistic Regression diperoleh akurasi nilai sebesar 94%.
MODEL SIMULASI PENYELESAIAN MASALAH PERJALANAN PENJUAL MENGGUNAKAN PENDEKATAN KECERDASAN BUATAN, OPTIMISASI KOLONI SEMUT Misinem, Misinem; Kurniawan, Tri Basuki; Astried, Astried; Widians, Joan Angelina
Jurnal Bina Komputer Vol 3 No 1 (2021): Jurnal Bina Komputer
Publisher : Jurnal Ilmiah Terpadu Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1624.207 KB) | DOI: 10.33557/binakomputer.v2i2.974

Abstract

Salah satu hal yang menarik dalam bidang perangkat lunak adalah ditemukannya algoritma pengoptimisasian. Banyak pekerjaan yang rumit dan kompleks yang akan mustahil untuk dilakukan secara manual ataupun kalau terpaksa dilakukan dengan cara manual akan membutuhkan waktu dan tenaga yang sangat besar. Dengan adanya algoritma optimisasian pekerjaan yang rumit dan kompleks tadi dapat dilakukan dengan lebih mudah dan lebih cepat. Bahkan juga memeberikan jaminan secara teoritis, untuk mendapatkan solusi yang terbaik. Dalam penelitian ini akan dibangun sebuah model simulasi perangkat lunak untuk menyelesaikan masalah perjalanan penjual dengan menggunakan algoritma optimisasi koloni semut, untuk memberikan visual bagi pengguna bagaimana masalah tersebut dapat diselesaikan secara Langkah demi Langkah. Pembangunan program simulasi menggunakan metode pengembangan perangkat lunak Extreme Programming (XP) pada lingkungan system operasi Windows dengan menggunakan Bahasa pemrograman C# pada Visual Studio 2019. Hasil dari penelitian didapati, program dapat memberikan visualisasi/simulasi yang baik kepada pengguna.
SISTEM PAKAR BAWANG DAYAK SEBAGAI OBAT ALTERNATIF Widians, Joan Angelina; Puspitasari, Novianti; Kurniawan, Tri Basuki
Jurnal Bina Komputer Vol 3 No 1 (2021): Jurnal Bina Komputer
Publisher : Jurnal Ilmiah Terpadu Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1111.735 KB) | DOI: 10.33557/binakomputer.v2i2.976

Abstract

The medicinal plants that have been developed especially in East Kalimantan is the dayak onion (Eleutherine palmifolia (L.) Merr). The Dayak onion herb has long been used by the Dayak tribe as an alternative medicine. Information of the possibility of people affected by the disease and how to process Dayak onion ingredients , an expert system needs to build that is able to diagnose the disease and how to process Dayak onion ingredients. The result of this research is an expert system with Certainty Factor that helps the general public in early diagnosis of ten diseases and provides alternative treatment solutions and ways of Dayak onion as an alternative medicine.
Unveiling Criminal Activity: a Social Media Mining Approach to Crime Prediction Armoogum, Sheeba; Dewi, Deshinta Arrova; Armoogum, Vinaye; Melanie, Nicolas; Kurniawan, Tri Basuki
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.350

Abstract

Social media platforms have become breeding grounds for abusive comments, necessitating the use of machine learning to detect harmful content. This study aims to predict abusive comments within a Mauritian context, focusing specifically on comments written in Mauritian Kreol, a language with limited natural language processing tools. The objective was to build and evaluate four machine learning models—Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine (SVM)—to accurately classify comments as abusive or non-abusive. The models were trained and tested using k-fold cross-validation, and the Decision Tree model outperformed others with 100% precision and recall, while Random Forest followed with 99% accuracy. Naïve Bayes and SVM, although achieving 100% precision, had lower recall rates of 35% and 16%, respectively, due to imbalanced data in the training set. Pre-processing steps, including stop-word removal and a custom Kreol spell checker, were key in enhancing model performance. The study provides a novel contribution by applying machine learning in a Mauritian context, demonstrating the potential of AI in detecting abusive language in underrepresented languages. Despite limitations such as the absence of a Kreol lemmatization tool and incomplete coverage of Kreol spelling variations, the models show promise for wider application in social media crime detection. Future research could explore expanding this approach to other languages and domains of social media crimes.
Analyzing Factors that Influence Student Performance in Academic Hidayani, Nieta; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.221

Abstract

Student performance analysis is a complex and popular study area in educational data mining. Multiple factors affect performance in nonlinear ways, making this topic more appealing to academics. The broad availability of educational datasets adds to this interest, particularly in online learning. Although previous studies have focused on analyzing and predicting students' performance based on their classroom activities, this study did not take into account student's outside conditions, such as sleep hours, extracurricular activities, and a sample of question papers that they had practiced.  These three variables are included among others in our study. In this paper, we describe an analysis of 10,000 student records, with each record containing information on numerous predictors and a performance index. The dataset intends to shed light on the relationship between predictor variables and the performance indicator. To create the correlation variable heatmap, we use both univariate and bivariate studies to produce a linear equation. Following that, we perform data preprocessing and modeling to facilitate predictive analysis. Finally, we showed the outcomes of actual and expected student performance using the model we constructed. The findings demonstrate that our prediction model was 98% accurate, with a mean absolute error of 1.62. 
Clustering the Unlabeled Data Using a Modified Cat Swarm Optimization Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Zakaria, Mohd Zaki; Armoogum, Sheeba
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.349

Abstract

This paper presents a modified version of the Cat Swarm Optimization (CSO) algorithm aimed at addressing the limitations of traditional clustering methods in handling complex, high-dimensional datasets. The primary objective of this research is to improve clustering accuracy and stability by eliminating the mixture ratio (MR), setting the counts of dimensions to change (CDC) to 100%, and incorporating a new search equation in the tracing mode of the CSO algorithm. To evaluate the performance of the modified algorithm, five classic datasets from the UCI Machine Learning Repository—namely Iris, Cancer, Glass, Wine, and Contraceptive Method Choice (CMC)—were used. The proposed algorithm was compared against K-Means and the original CSO. Performance metrics such as intra-cluster distance, standard deviation, and F- measure were used to assess the quality of clustering. The results demonstrated that the modified CSO consistently outperformed the competing algorithms. For example, on the Iris dataset, the modified CSO achieved a best intra-cluster distance of 96.78 and an F-measure of 0.786, compared to 97.12 and 0.781 for K-Means. Similarly, for the Wine dataset, the modified CSO reached a best intra-cluster distance of 16399, surpassing K-Means which recorded 16768. In conclusion, the modifications introduced to the CSO algorithm significantly enhance its clustering performance across diverse datasets, producing tighter and more accurate clusters with improved stability. These findings suggest that the modified CSO is a robust and effective tool for data clustering tasks, particularly in high-dimensional spaces. Future work will focus on dynamic parameter tuning and testing the scalability of the algorithm on larger and more complex datasets.
Gum Disease Identification Using Fuzzy Expert System Nasir, Muhammad; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Bujang, Nurul Shaira Binti
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.346

Abstract

Gum disease, including Gingivitis and Periodontitis, is among the most common dental conditions, primarily caused by dental plaque, a bacterial biofilm. These conditions are strongly linked to various systemic illnesses, including cancer, atherosclerosis, hypertension, stroke, and respiratory and cardiovascular conditions like aspiration pneumonia, as well as adverse pregnancy outcomes. Gum inflammation is typically characterized by symptoms such as increased redness, swelling (edema), and a loss of surface texture (stippling; gum fiber attachment). These symptoms are site-specific, meaning that an individual can have both healthy and diseased areas within their mouth. In this research, we developed a fuzzy expert system using MATLAB to identify gum diseases. The system was tested on various cases and produced an output value of 0.133, which successfully identified Gingivitis. This value was derived using a fuzzy logic system that processes input data through predefined rules within the Fuzzy Expert System (FES). The system utilizes several input variables such as the frequency of gum bleeding, the extent of plaque accumulation, the depth of gum recession, and the degree of tooth mobility. The key contribution of this study lies in the integration of fuzzy logic to handle the inherent uncertainties in clinical diagnosis, providing a more nuanced assessment compared to traditional methods. The novelty of this research is the application of a fuzzy expert system in dental diagnostics, offering a promising tool for improving the accuracy and efficiency of gum disease identification in clinical settings. This system has the potential to assist dentists in making more informed decisions, ultimately leading to better patient outcomes.
Co-Authors - Kurniawan, - Adi Wijaya Agus Riyanto Alde Alanda, Alde Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Antoni, Darius Armoogum, Sheeba Armoogum, Vinaye Asro, Asro Astried, Astried Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bujang, Nurul Shaira Binti Chandra, Anurag Dedy Syamsuar Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Diana Diana Edi Surya Negara Eko Risdianto Fadly Fadly Fatoni, Fatoni Febriyanti Panjaitan Firosha, Ardian Fuad, Eyna Fahera Binti Eddie Habib, Shabana Hadi Syahputra Hanan, Nur Syuhana binti Abd Hasibuan, M.S. Henderi . Hendra Kurniawan Herdiansyah, M. Izman Hidayani, Nieta Hisham, Putri Aisha Athira binti Irianto, Suhendro Y. Irwansyah Irwansyah Ismail, Abdul Azim Bin Isnawijaya, Isnawijaya Joan Angelina Widians, Joan Angelina Kijsomporn, Jureerat Kurniawan, Dendi Lexianingrum, Siti Rahayu Pratami M Said Hasibuan Madjid, Fadel Muhammad Maizary, Ary Mantena, Jeevana Sujitha Mashal Alqudah Melanie, Nicolas Misinem, Misinem Mohd Salikon, Mohd Zaki Motean, Kezhilen Muhamad Akbar Muhammad Islam, Muhammad Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Nathan, Yogeswaran Nazmi, Che Mohd Alif Oktariansyah Oktariansyah, Oktariansyah Onn, Choo Wou Periasamy, Jeyarani Prahartiningsyah, Anggari Ayu Praveen, S Phani Puspitasari, Novianti Qisthiano, M Riski R Rizal Isnanto Rahmi Rahmi RR. Ella Evrita Hestiandari Saksono, Prihambodo Hendro Saringat, Zainuri Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Sri Karnila Sulaiman, Agus Sunda Ariana, Sunda Suriani, Uci Syaputra, Hadi Taqwa, Dwi Muhammad Thinakaran, Rajermani Triloka, Joko Udariansyah, Devi Usman Ependi Wibaselppa, Anggawidia Yeh, Ming-Lang Yorman Yupika Maryansyah, Yupika Zakari, Mohd Zaki Zakaria, Mohd Zaki