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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jiko (Jurnal Informatika dan komputer) Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali JURNAL IT: Media Infromasi STMIK Handayani Makassar Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Ilmu Komputer untuk Masyarakat Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Journal of Technology Research in Information System and Engineering Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali)
Nur, M. Adnan
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Public Health Other

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Mobile Application Bimbingan Tugas Akhir Mahasiswa Pada Stmik Handayani Makassar Sebagai Media Pendukung Pembelajaran Daring M. Adnan Nur; Nurilmiyanti Wardhani
Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Vol 6, No 4 (2021): Jurnal Elektroda Vol 6 No 4
Publisher : Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/jfe.v6i4.20608

Abstract

Penelitian ini bertujuan untuk mengembangkan serta menguji akses antara mobile application yang dibangun dengan framework apache cordova dan web server apache melalui hypertext transfer protocol (HTTP) menggunakan Object XHR dari Javascript. Aplikasi yang dibangun menggunakan studi kasus bimbingan tugas akhir pada STMIK Handayani Makassar. Penelitian ini dilaksanakan menggunakan metode research and  development  (R & D) yang terdiri atas beberapa tahapan yaitu tahap analisis meliputi tinjauan terhadap penggunaan framework apache cordova dan web service, tahap perancangan menggunakan unified modelling language, tahap pembuatan aplikasi dan tahap pengujian menggunakan metode blackbox. Hasil penelitian menunjukkan bahwa seluruh fungsi aplikasi dapat mengakses data dari web service. Terdapat perbedaan waktu request data server ketika aplikasi dijalankan pada emulator (google chrome) dan aplikasi yang diinstalasi langsung pada perangkat android dengan sistem operasi MIUI OS dan ColorOS. Request data server dengan emulator lebih cepat dengan perbedaan waktu yang tidak begitu signifikan yaitu rata-rata selisih waktu hanya 41,76 ms untuk MIUI OS dan 46,52 ms  untuk ColorOS.
Optimasi Normalisasi Kata Pada Data Twitter Untuk Meningkatkan Akurasi Analisis Sentimen (Studi Kasus Respon Masyarakat Terhadap Layanan Teman Bus) M. Adnan Nur; Nurilmiyanti Wardhani
Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Vol. 7 No. 4 (2022): Jurnal Fokus Elektroda Vol 7 No 4 2022
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (854.72 KB) | DOI: 10.33772/jfe.v7i4.21

Abstract

Teman Bus merupakan layanan yang disediakan oleh Kementerian Perhubungan RI. Untuk mengetahui tingkat kepuasan masyarakat terhadap layanan Teman Bus, Analsis sentimen dapat diterapkan menggunakan data media sosial twitter. Teks yang tidak terstruktur pada twitter menjadi permasalahan dalam analsis sentimen khsusnya kesalahan ejaan kata dan penggunaan kata slang. Tujuan dari penelitian ini adalah melakukan optimasi normalisasi kata dengan menerapkan koreksi ejaan kata dan konversi kata slang menjadi kata baku. Tahapan penelitian terdiri atas pengumpulan data, prapemrosesan, analsis sentimen dan pengujian akurasi. Data yang digunakan terdiri atas data twitter dengan kata kunci teman bus, dataset kata dasar (kata baku) dan dataset kata slang. Untuk prapemrosesan, tahapannya meliputi tokenizing, case folding, filtering, stemming, koreksi ejaan kata dengan levenshtein distance dan konversi kata slang dengan model word2vec. Pembagian data latih dan data uji untuk pengujian klasifikasi analisis sentimen menggunakan k-fold cross validation. Tahap pengujian disiapkan 5 skenario pengujian dengan pengaturan parameter levenshtein distance dan word2vec serta terdapat 1 skenario pengujian yang tidak melibatkan normalisasi kata. Hasil yang diperoleh pada tahap pengujian menunjukkan peningkatan akurasi dengan menerapkan normalisasi kata sebesar 0,776. Normalisasi kata ini menggunakan ratio levenshtein distance sebesar 0,9 dan min-count word2vec sebesar 10.
Implementasi Metode Double Exponential Smoothing Untuk Memprediksi Indeks Pembangunan Manusia (IPM) Di Kabupaten Toraja Utara Adistacia Caesara Ampang; Billy Eden William Asrul; M. Adnan Nur
Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Vol. 8 No. 3 (2023): Jurnal Fokus Elektroda Vol 8 No 3 2023
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Halu Oleo

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Abstract

Abstract — The Human Development Index (IPM) is an indicator that explains how people can access development outcomes in obtaining human income, health and education. This study aims to make predictions on the Human Development Index (IPM) in North Toraja Regency using the Double Exponential Smoothing algorithm. The stages of this research began with collecting data obtained from observation, interviews and documentation. The research design used is UML which is designed in a structured manner consisting of use case diagrams, activity diagrams, sequence diagrams and class diagrams. The software used in building this system is PHP and MySql for database processing. The algorithm used is Double exponential smoothing to predict the Human Development Index in North Toraja Regency for the next few years using past data. In this case, the best alpha and beta values are 0.6 and 0.9. The results of calculating the accuracy between the prediction results and the actual data using the Mean Absolute Percetage Error (MAPE) each have a forecasting error value of 0.13% for the Health Index, for the education index of 0.55%, and for the Purchasing Power Index of 0.45 %. Based on the results of these predictions, the value of the Human Development Index (IPM) in North Toraja Regency for the following year is 69.48%. Keywords: Double Exponential Smoothing, Human Development Index, prediction, MAPE, North Toraja Regency
Implementasi Algoritma Profile Matching Untuk Kelayakan Ujian Pada Universitas Handayani Makassar Andi Alamsyah; Najirah Umar; M Adnan Nur
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The 1945 Constitution guarantees equal education for all Indonesian citizens, but 12 years of compulsory education is still not enough to cope with the increasing demands of the labor market. This makes it difficult for the younger generation to find jobs that match their expectations. Handayani University Makassar plays a role in helping to overcome these problems. Currently, the process of determining college eligibility is still manual and complicated. This research proposes a solution using technology through profile matching on web applications. This method determines test eligibility based on predetermined attribute weights. This helps students assess their potential before registering for the exam. The application of profile matching algorithm will also help universities identify students who are likely to pass the exam. This will speed up decision-making and improve student progression and learning outcomes. This research aims to predict students' exam eligibility using the profile match method. So that it is expected to help students achieve maximum passing standards and complete their studies on time. The application of the profile match algorithm is also expected to overcome the problem of slow decision making, help identify outstanding students, and improve student development and learning outcomes. Therefore, this research is titled "Implementation of profile comparison algorithm to assess exam eligibility".
Implementasi Algoritma Genetika Untuk Penjadwalan Ujian Pada Universitas Handayani Makassar Syahrul Saleh; Najirah Umar; M Adnan Nur
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research relates to Handayani University Makassar formerly STMIK Handayani Makassar. The university offers various programs in the field of computer science and IT management. One of the main problems faced is that the preparation of the exam schedule is currently still done manually using Microsoft Excel, prone to errors and time consuming. This research aims to develop an automated system for preparing exam schedules using genetic algorithms. Genetic algorithm is a computer method that helps find optimal solutions in exam scheduling. The results of this study are expected to help improve the efficiency and accuracy of the preparation of the exam schedule at Handayani University Makassar.
EVALUATION OF INDOBERT AND ROBERTA: PERFORMANCE OF INDONESIAN LANGUAGE TRANSFORMER MODELS IN SENTIMENT CLASSIFICATION Nur, M. Adnan; Umar, Najirah; Feng, Zhipeng; Gani, Hamdan
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.9988

Abstract

The development of Natural Language Processing (NLP) technology has had a significant impact on various fields, especially in sentiment analysis. This analysis becomes important in understanding public perception, especially on social media which has a lot of opinions. Indonesian, with its morphological complexity, dialectal variations, and dynamic everyday vocabulary usage, presents unique challenges in the development of NLP models. This study aims to evaluate and compare the performance of two Indonesian language transformer models, namely IndoBERT (Indonesia Bidirectional Encoder Representations from Transformers) and RoBERTa Indonesia (Robustly Optimized BERT Pretraining Approach) in applying sentiment classification using the Indonesian General Sentiment Analysis Dataset. Both models were fine-tuned using consistent hyperparameter configurations to ensure the validity of the comparison. Evaluation was conducted based on classification metrics, namely precision, recall, F1-score, and accuracy. The results show that the IndoBERT model excels in all aspects of evaluation. IndoBERT achieved an accuracy of 70%, while RoBERTa Indonesia only reached 67%. Additionally, the average F1-score of IndoBERT at 0.69 is higher compared to RoBERTa, which only reached 0.65. The performance of IndoBERT is also more balanced in classifying the three sentiment categories (negative, neutral, and positive), whereas RoBERTa shows less consistent performance, especially in negative and positive sentiments. In the loss analysis, IndoBERT produced a lower evaluation loss value, indicating better generalization capability. Additionally, IndoBERT also shows faster and more stable training times compared to RoBERTa. This performance difference shows that the architecture and pre-trained data used by each model affect their ability to understand Indonesian contextually. This research provides a comprehensive comparative overview of the effectiveness of two transformer models in the task of Indonesian language sentiment analysis, as well as lays the groundwork for selecting a more optimal model in the development of NLP systems for social media.
PEMANFAATAN TOOLS AI DALAM MENINGKATKAN KUALITAS PEMBELAJARAN DI SD NEGERI 115 BENTENG GAJAH KABUPATEN MAROS Zuhriyah, Sitti; Nur, M. Adnan; Namruddin, Respaty; wardhani, Nurilmiyanti
Ilmu Komputer untuk Masyarakat Vol 6, No 1 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v6i1.2805

Abstract

Penerapan teknologi Artificial Intelligence (AI) dalam dunia pendidikan menjadi sangat relevan untuk menjawab tantangan pembelajaran masa kini. Salah satu tools AI yang dapat dimanfaatkan oleh guru adalah platform Quizizz, yang menggabungkan unsur interaktivitas, gamifikasi, dan analitik berbasis data. Quizizz memungkinkan guru membuat kuis interaktif yang sesuai dengan kebutuhan siswa, memberikan umpan balik secara langsung, serta memantau kemajuan belajar siswa secara real-time. Kegiatan pengabdian kepada masyarakat ini dilaksanakan di SD Negeri 115 Benteng Gajah, Kabupaten Maros, dengan tujuan meningkatkan pemahaman dan keterampilan guru dalam memanfaatkan Quizizz sebagai media pembelajaran. Metode pelaksanaan meliputi identifikasi kebutuhan, penyusunan materi, pelatihan, dan evaluasi. Evaluasi dilakukan menggunakan kuesioner terhadap guru dan tenaga kependidikan sebagai responden. Hasil kegiatan menunjukkan adanya peningkatan signifikan dalam literasi digital dan keterampilan guru, khususnya dalam mengimplementasikan teknologipembelajaran berbasis AI. Guru juga menunjukkan motivasi tinggi untuk mengadopsi metode pembelajaran yang lebih interaktif dan adaptif. Kegiatan ini berhasil menjawab permasalahan mitra dan memberikan dampak positif yang nyata terhadap proses belajar mengajar. Intervensi pelatihan teknologi seperti ini dapat menjadi strategi yang efektif untuk meningkatkan kualitas pembelajaran di tingkat sekolah dasar.
Feature Extraction Optimization to Improve Naïve Bayes Accuracy in Sentiment Analysis of Bulukumba Tourism Objects Setiawan, Darmawan; Umar, Najirah; Nur, M. Adnan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4580

Abstract

This research employs social media (Twitter) to apply sentiment analysis ascertain the degree of public satisfaction with the Bulukumba tourist attraction. Unstructured text data is a major challenge in sentiment analysis. For this reason, implementing the Naïve Bayes algorithm is an effective approach for conquering this challenge because of its ability to handle text data well. This study aims to evaluate the performance of multinomial Naïve Bayes by testing a combination of minimum document frequency (min-df) and maximum document frequency (max-df) parameter values in determining the level of accuracy. This analysis stage includes collecting data from Twitter related to the Bulukumba tourist attraction. Preprocessing carried out includes data cleaning, casefolding, text normalization, tokenization, stopword removal, and stemming. Feature extraction using Count Vectorizer and TF-IDF weighting. The process ends with 10-Fold Cross-Validation by separating the data into training data and test data for sentiment analysis classification, as well as evaluation using the Confusion Matrix. In this research, there are 10 test scenarios with various combinations of min-df and max-df. The values of employed min-df consists of 0.001, 0.002, 0.005, 0.01, 0.02 and max-df consists of 0.5 and 0.8. The results of implementing Multinomial Naïve Bayes in this test show that classification accuracy increases with effective min-df and max-df parameter settings. The greatest accuracy was 0.7910 in testing a combination of min-df parameter values of 0.001 and max-df 0.8. Meanwhile, the average accuracy for each test was obtained the highest value of 0.7272 with min-df of 0.002 and max-df of 0.5 and 0.8 respectively.
Digital Image-based Classification of Clove Quality using Naïve Bayes Algorithm Dilla, Dilla; Nur, M. Adnan; Djamaluddin, Musdalifah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4617

Abstract

At present, clove cultivation is increasingly in demand, especially by farmers because it is easy to maintain and the selling price is high. Researchers conducted observations in Tana Toa Village on how the processing of cloves after harvesting is drying them in the sun until they turn brown and shrink. After that, farmers select dried cloves and distinguish between good and bad quality. One way for farmers and traders to determine the quality of cloves is by visually inspecting the size and color. One of the disadvantages of this manual classification process is that each person can look at the same material in bulk in different ways depending on the situation or individual weak points. The aim of this research is to help farmers produce high quality cloves that will ultimately produce favorable results on their economy. With digital image-based methods and Naive Bayes, this process can be done quickly and efficiently, reducing operational costs and labor time. The Naive Bayes algorithm is able to process data more thoroughly than humans, especially if the image quality and features used for classification are optimized. This reduces human errors that may occur during manual processing. The results of this study are, Gaussian Naive Bayes testing has an accuracy of 0.82. Bernoulli naïve bayes has an accuracy of 0.69, Complement naïve bayes and multinomial naïve bayes each have an accuracy of 0.89. This shows that they affect the accuracy rate of clove quality effectively
Co-Authors Adistacia Caesara Ampang Andi Alamsyah Billy Eden William Asrul Darmawan Setiawan, Darmawan Dilla, Dilla Djamaluddin, Musdalifah Feng, Zhipeng Gani, Hamdan Herlina Ahmad Luh Putu Ratna Sundari Najirah Umar Namruddin, Respaty Nur, Muhammad Ardi Syahrul Saleh Wardhani, Nurilmiyanti Zuhriyah, Sitti