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All Journal Jurnal Informatika Upgris JURNAL MEDIA INFORMATIKA BUDIDARMA ILKOM Jurnal Ilmiah Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar JSAI (Journal Scientific and Applied Informatics) Jambura Journal of Electrical and Electronics Engineering JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Nasional Cosphi Nusantara of Engineering (NOE) Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Jurnal Rekayasa elektrika
Haditsah Annur
Universitas Ichsan Gorontalo
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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PENERAPAN NETWORK MONITORING SYSTEM (NMS) SECARA VISUAL PADA INFRASTRUKTUR JARINGAN FISIK BERBASIS WEB Annur, Haditsah; Laari, Ramdan A
Nusantara of Engineering (NOE) Vol 5 No 2 (2022): Volume 5 No 2 Tahun 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/noe.v5i2.18682

Abstract

A Network Monitoring System (NMS) is a system that continuously monitors the network and provides immediate notification to network administrators in the event of problems or interruptions. This study aims to apply a Network Monitoring System for monitoring network devices in Universitas Ichsan Gorontalo. This application is visually web-based, so network administrators can easily find problems with network devices in this system. As a result of this study, the system can now find problematic network devices on existing network devices. It can show the results in a visual format. If the network device check fails, the system recognizes if there is a problem with the network device.
Analisis Keranjang Belanja Pelanggan Coffe Shop Menggunakan Algoritma FP-GROWTH haditsah annur; Serwin Serwin; Intan Nur Anisa
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.8835

Abstract

One of the businesses that attracts economic development is a coffee shop. This business is very important and growing rapidly. Shopping cart analysis has the ability to provide information about which products are frequently purchased together. The use of shopping cart analysis is regulated in association rules which is a data processing process that provides records of purchase transactions that come out simultaneously at one time, the algorithm used to regulate these association rules is the FP-Growth algorithm. coffee shop customer shopping cart analysis uses the FP-Growth Algorithm. This research data was obtained from public data on the website https://www.kaggle.com/datasets/sryasuka/coffee-shop-dataset/data,, with a dataset of 1000 transactions, the data processing uses RapidMiner tools, after processing, 2 association rules were found using minimum support = 0.01 and minimum confidence = 0.7. It can be concluded that the results of the shopping cart analysis show that 1 item is most frequently purchased by customers, namely croissants and the purchase of 2 items, namely croissants and fries. So that the shopping basket analysis method with the FP-Growth algorithm can optimize item combination patterns and can improve sales strategies, thereby supporting coffee shop business decision making.
Implementasi Deteksi Kemiripan Judul Skripsi Dengan Menggunakan Algoritma Winnwong Mohamat Hamet Helingo; Haditsah Annur; Sudirman S Panna
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 5 No 1 (2026): Mei 2026
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/j21ngh85

Abstract

Penelitian ini berujuan untuk mengimplementasikan algoritma Winnowing dalam sistem deteksi kemiripan judul skripsi pada Fakultas Ilmu Komputer Universitas Ichsan Gorontalo. Algoritma Winnowing dipilih karena kemampuannya yang efektif dalam mengidentifikasi kemiripan teks. Sistem ini dirancang untuk membandingkan judul skripsi baru dengan judul-judul yang sudah ada dalam database, dan memberikan output berupa tingkat kemiripan serta keputusan apakah judul tersebut diterima atau ditolak berdasarkan range minimal 30%. Proses evaluasi sistem dilakukan melalui pengujian black box dan white box. Pengujian black box digunakan untuk menguji fungsionalitas sistem berdasarkan perancangan, sementara pengujian white box digunakan untuk memeriksa alur logika sistem. Hasil evaluasi menunjukan bahwa implementasi sistem deteksi kemiripan judul skripsi dengan menggunakan algoritma Winnowing telah sesuai dengan perancangan. Pengujian white box menghasilkan nilai Cyclomatic Complexity (CC) sebesar 4, yang menunjukkan bahwa alur logika sistem telah berjalan dengan benar. Dengan demikian, sistem deteksi kemiripan judul skripsi ini layak diimplementasikan di Fakultas Ilmu Komputer Universitas Ichsan Gorontalo. Sistem ini diharapkan dapat membantu dalam proses seleksi judul skripsi dengan lebih efisien dan mengurangi potensi terjadinya duplikasi judul
Prediksi Hasil Panen Biji Cengkeh Menggunakan Metode K-Nearest Neighbor Sitti Khairunnisa S Musa; Haditsah Annur; Apritanto Alhamad
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 5 No 1 (2026): Mei 2026
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/78wqq036

Abstract

Prediksi hasil panen biji cengkeh merupakan aspek penting dalam meningkatkan produktivitas pertanian. Metode K-Nearest Neighbor (KNN) telah terbukti efektif dalam memprediksi hasil panen tanaman. Studi ini mengevaluasi penerapan metode KNN untuk memprediksi hasil panen biji cengkeh. Data historis hasil panen biji cengkeh digunakan untuk melatih dan menguji model KNN. Hasil penelitian menunjukkan bahwa metode KNN memberikan prediksi yang mendekati nilai sebenarnya dari hasil panen biji cengkeh. Analisis kinerja model menunjukkan bahwa menggunakan nilai K=3 menghasilkan kinerja terbaik, dengan Mean Absolute Error (MAE) sebesar 49.494 dan Mean Squared Error (MSE) sebesar 4.3240,33. Hal ini mengindikasikan bahwa model KNN dengan K=3 dapat memberikan prediksi yang paling akurat untuk hasil panen biji cengkeh. Implementasi metode KNN dalam prediksi hasil panen biji cengkeh menandakan efisiensi dalam memanfaatkan data yang tersedia. Kesimpulannya, penerapan metode KNN dalam prediksi hasil panen biji cengkeh dapat diterapkan secara efektif untuk meningkatkan produktivitas dan efisiensi dalam pertanian biji cengkeh. 
Analisis Perbandingan Decision Tree dan Random Forest Untuk Prediksi Penyakit Berdasarkan Gejala Pasien Sunarto Taliki; Serwin Serwin; Haditsah Annur; Mohamad Rayhan A Ismail
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.37934

Abstract

Basic principles of public health form a crucial foundation in efforts to protect and improve public health, with a primary focus on disease prevention. In the era of transformation, a science capable of prediction is needed, such as the use of decision tree and random forest algorithms, which have the ability to generate accurate predictions through data processing by forming decision trees. This research utilizes a public dataset on "Peduli Sehat," collected from https://www.kaggle.com/datasets/krismonosadi/peduli-sehat-dataset, consisting of 4,921 records. The research problem is to determine which algorithm provides the best performance based on a dataset of 130 disease symptoms. Therefore, this study aims to explore data mining techniques using a comparative performance approach between the decision tree algorithm and the random forest algorithm in making predictions. Data analysis was conducted using a 70:30 validation split test to identify the best performance for disease prediction. The research results show that the decision tree algorithm achieved a performance of 94.17% accuracy, 95.04% precision, and 94.55% recall, while the random forest algorithm performance was 44.65% accuracy, 45.88% precision, and 46.67% recall. Therefore, this study proves that the decision tree algorithm is more effective for datasets with many symptom features but linear patterns compared to the random forest algorithm. Additionally, the results obtained can provide scientific contributions, particularly in the field of machine learning, and serve as a reference for scientific development in the health sector.
PENERAPAN ALGORITMA NAIVE BAYES BERBASIS FORWARD SELECTION UNTUK MEMPREDIKSI PENJUALAN MOBIL BEKAS annur, haditsah; Moh.Efendi Lasulika
JURNAL ILMU KOMPUTER Vol 10 No 2 (2024): Edisi September
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v10i2.320

Abstract

Cars are one of the vehicles that are the daily needs of the people, not only the use of new cars is in demand, but now used cars are also in great demand because the quality of used cars is still good and the many types of used cars are sold in the market. The aim of the researchers is to increase public interest in switching to buying used cars. This study uses data mining methods, one of which is prediction using the Naive Bayes algorithm as an algorithm that uses probabilistic and statistical methods to predict the future, besides that the data is also processed using forward selection feature selection which aims to reduce the level of complexity of a classification algorithm while increasing accuracy. The research data used were 2318 records, in this study an experiment was carried out with the accuracy results obtained using split validation on the naive Bayes algorithm of 96.98% and then another experiment was carried out to obtain accurate results using split validation on the naive bayes algorithm based on forward selection of 97.82 %. Thus the naive Bayes algorithm based on forward selection is suitable for predicting, as well as being used for handling in the future that there are still many used cars that are of interest to the public..
Co-Authors ABDUL_MUHRIZAL_ZULKIFLI_H_MARADA Andri Setiawan Apritanto Alhamad Apriyanto Alhamad Asmaul Husnah Nasrullah Balok, Admin Caldiyastovan Mohi Intan Nur Anisa Irma Surya Kumala Idris Laari, Ramdan A Moh.Efendi Lasulika Mohamad Efendi Lasulika Mohamad Rayhan A Ismail Mohamat Hamet Helingo Muammar Zainuddin Nur Insani Panna, sudirman S. Rachmawan Sidik Laminullah Rahma Rizal Roys Pakaya Rustam, Suhardi Serwin Sitti Khairunnisa S Musa Sudirman Melangi Sudirman S. Panna Suhardi Rustam Sunarto Taliki Warid Yunus Yusrianto Malago