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All Journal Jurnal Ilmu Kelautan Spermonde Jurnal Teknik Informatika (JUTIF)
Ahmad Bahar
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Computer Science & IT Earth & Planetary Sciences

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NUDIBRANCHIA DENSITY AND DIVERSITY IN SPERMONDE ISLANDS, SOUTH SULAWESI Asrul, Asrul; Abdul Haris; Ahmad Bahar; Syafiuddin; Yasir, Inayah
Jurnal Ilmu Kelautan SPERMONDE VOLUME 7 NUMBER 2, 2021
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jiks.v7i2.18636

Abstract

Nudibranchia is one of the interesting and unique organisms because it has different shapes, sizes and colors attracting tourists when doing snorkeling and diving activities. However, because the distribution of marine biota is very dependent on habitat conditions and the availability of food types, it affects the density and diversity of Nudibranchia in the waters. The purpose of this study was to determine the density and diversity of Nudibranchia species. This research was conducted in March-April 2021 on Samalona Island, Barranglompo Island, and Badi Island in the Spermonde Islands, South Sulawesi. The method used is the Belt Tansect method with a length of 100 m with a sweep of 2.5 m to the left and right, at a depth of 4-7 m. Based on the results of the study, the number of Nudibranchia species found on Samalona Island was 4 families, 7 species, and 36 individuals; on Barranglompo Island as many as 4 families, 6 species, and 51 individuals; while on Badi Island there are 3 families, 6 species, and 30 individuals. Nudibranchia density on Samalona Island is 0.024 ind/m2, on Barranglompo Island is 0.034 ind/m2, while on Badi Island it is 0.020 ind/m2. The value of the Nudibranchia Diversity Index on Samalona Island is 1.14; on Barranglompo Island by 0.96; and on Badi Island it was 1.28 with a diversity community structure that was quite stable to stable on each island. Substrate cover conditions found on each island were dominated by Dead Coral Algae (DCA), on Samalona Island at 58.33%; Barranglompo Island by 54.90%; and Badi Island by 66.67%. Keywords: Nudibranchia, density, diversity, Samalona Barranglompo, Badi.
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.
Co-Authors Asrul Asrul Inayah Yasir Jayawarsa, A.A. Ketut Primandani Arsi Syafiuddin TRI ASTUTI