Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Kelautan Spermonde Jurnal Teknik Informatika (JUTIF)
Ahmad Bahar
Unknown Affiliation
Computer Science & IT Earth & Planetary Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PERFORMANCE COMPARISON OF SVM, NAIVE BAYES, AND LOGISTIC REGRESSION CLASSIFICATION ALGORITHMS IN ANALYZING NOICE APP USER REVIEWS Ahmad Bahar; Tri Astuti; Primandani Arsi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2061

Abstract

In the rapidly growing digital era, user reviews on distribution platforms such as the Google Play Store are a key indicator in assessing the popularity, quality, and user satisfaction of applications. This study aims to compare the performance of SVM, Naive Bayes, and Logistic Regression classification algorithms in analyzing user reviews of the Noice app, an audio content platform. The research involves steps such as data collection, data pre-processing, word embedding, modeling, model evaluation, and sentiment analysis. Testing was conducted using 1877 data. The data from the reviews were divided into scenarios, with training and testing data divided in ratios of 90:10, 80:20, and 70:30. The results showed that the SVM algorithm achieved the highest accuracy rate (80%) in the 90:10 data split scenario. However, Naive Bayes also showed competitive results with 78% accuracy in the same scenario. Meanwhile, Logistic Regression achieved 78% accuracy when the data was split in an 80:20 ratio. Evaluation was done using metrics such as accuracy, precision, recall, and F1-score. Sentiment analysis showed a positive trend with 1194 positive data compared to 683 negative data. From the comparison of data sharing scenarios and algorithms, SVM at 90:10 data sharing gave the best results.
DIVERSITY, DISTRIBUTION AND CONSERVATION STATUS OF SHARK SPECIES FROM THE WATERS OF LANGKAI ISLAND, SPERMONDE ARCHIPELAGO Aidah Ambo Ala Husain; Andi Muhammad Subhan; Andi Iqbal Burhanuddin; Budimawan Budimawan; Ahmad Bahar
Jurnal Ilmu Kelautan SPERMONDE VOLUME 12 NOMOR 1, 2026
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35911/jiks.v12i1.50389

Abstract

Sharks as top predators in marine waters have important role in balancing the ecosystem, however they were suffering from high overfishing fishery. The study was conducted to identify species composition and sex determination observed sharks as well as mapping their distribution, and to define interviewed by-catch sharks into their conservation status based on the IUCN Red List. This research was carried out in April 2021 in the adjacent waters of Langkai Island, Spermonde Archipelago. A survey method was carried out to gather fisheries data and biological aspects of catches in 11 trips, as well as conducting interviews as secondary data needs. There were 20 individuals of identified shark with a length range of 58-131 cm, and within composition of five species: Chiloscyllium punctatum (45%), Triaenodon obesus (25%), Carcharhinus melanopterus (20%), C. falciformis (5%), and C. sealei (5%). The female sharks composition was higher (65%) than male ones (35%). The observed sharks were fished in the adjacent water of Langkai Island within distances of 0.20-8.25 km to the southwest of the island. Meanwhile, there had been 12 species of by-catch sharks from the fishermen interviews, which were classified into conservation status of Near Threatened (58%), Vulnerable (17%), Endangered (17%), and Critically Endangered (8%).
Co-Authors Aidah Ambo Ala Husain Andi Iqbal Burhanuddin Budimawan Budimawan Primandani Arsi Subhan, Andi Muhammad TRI ASTUTI