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All Journal JSEH (Jurnal Sosial Ekonomi dan Humaniora) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Language and Literature Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Journal of Electrical Engineering and Computer (JEECOM) Jurnal Pengabdian Pada Masyarakat IPTEKS Mabasan
Fahry, Fahry
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Analysis of Tourist Sentiment towards Tourist Attractions in the Mandalika Special Economic Zone Using the Naïve Bayes Method Pribadi, Teguh Iman; Fahry, Fahry; Muharis, Muharis; Marswandi, Ega Dwi Putri
Jurnal Bumigora Information Technology (BITe) Vol 6 No 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i1.4081

Abstract

The Mandalika Special Economic Zone has become one of the most popular destinations for both domestic and international tourists. This popularity highlights the importance of understanding the views and feelings of the tourists. Therefore, this study aims to analyze tourist sentiment towards the attractions in the Mandalika Special Economic Zone. The data analyzed was obtained from 1,144 reviews on the TripAdvisor platform. The research stages included data collection, data labeling, data preprocessing, data transformation, data classification, as well as data analysis and visualization. The results of this study indicate that the majority of tourists have a positive sentiment towards the attractions in the Mandalika Special Economic Zone. Furthermore, testing with the Naïve Bayes algorithm successfully classified tourist sentiments accurately, with consistent accuracy rates obtained from each fold: fold 1: 89.08%, fold 2: 89.96%, fold 3: 88.21%, fold 4: 87.34%, and fold 5: 90.79%.
Peningkatan Transparansi Keuangan: Implementasi Sistem Informasi Akuntansi Berbasis Cloud Komala, Rina; Fahry, Fahry; Elisa, Nopa
JURNAL SOSIAL EKONOMI DAN HUMANIORA Vol. 10 No. 4 (2024): JURNAL SOSIAL EKONOMI DAN HUMANIORA
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jseh.v10i4.702

Abstract

This study explores the impact of implementing a cloud-based accounting information system on financial transparency in the tourism sector of Lombok Island. A mixed-methods approach was employed, including quantitative analysis of 120 businesses and qualitative interviews with 25 informants. The results revealed that 90% of respondents reported improved financial transparency after adopting cloud systems, highlighting operational efficiency, reporting accuracy, and ease of data access. However, challenges such as implementation costs, limited internet infrastructure, and a lack of local expertise remain significant barriers. This study recommends local training, subsidized implementation costs, and improved infrastructure to accelerate adoption. Cloud systems are expected to enhance not only financial transparency but also the competitiveness of small and medium enterprises in Lombok's tourism sector. This research contributes to developing relevant financial technology strategies to support digital transformation in Indonesia's tourism industry. 
Expert System for Skin Disease Diagnosis Using the Best First Search Method and Fuzzy Tsukamoto Fahry, Fahry; Adam, M. Awaludin; Hidjah, Khasnur; Azwar, Muhammad; Hairani, Hairani
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10981

Abstract

The skin is the largest organ and is vulnerable to various diseases, which can spread through direct contact or the environment. Skin diseases are among the ten most common conditions in outpatient care in Indonesia, often caused by poor hygiene and environmental exposure. The limited number of dermatologists makes diagnosing and treating skin diseases more challenging. This study develops an expert system for diagnosing skin diseases using the Best First Search method and Fuzzy Tsukamoto, serving as an alternative or complement to medical diagnosis. Best First Search prioritizes diagnoses based on predefined rules, while Fuzzy Tsukamoto adds flexibility in assessing disease severity. Testing shows that the system achieves an accuracy of 83.3%, demonstrating its potential to assist patients and medical professionals in improving diagnostic efficiency and healthcare quality for skin diseases.
Pelatihan Sistem Manajemen Aplikasi SISKA Untuk Pengisian KRS Mahasiswa Berbasis WEB Di Universitas Bumigora Fahry, Fahry; Febriana, Widia; Asroni, Ondi; Urfina, Yasyifa Dian
Jurnal Pengabdian Pada Masyarakat IPTEKS Vol. 2 No. 1: Jurnal Pengabdian Pada Masyarakat IPTEKS, Desember 2024
Publisher : CV. Global Cendekia Inti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71094/jppmi.v2i1.88

Abstract

SISKA Application Management System (Study Plan Card Filling Information System) is a web-based application designed to facilitate the process of filling out student Study Plan Cards (KRS) at Bumigora University. This application aims to improve the efficiency and effectiveness of the KRS filling process, as well as facilitate students in accessing academic information. By using web-based technology, the SISKA application allows students to access and fill out KRS online, as well as monitor the status of KRS filling in real-time. In addition, this application can also assist lecturers and academic staff in managing KRS data and monitoring student academic progress. SISKA application was developed using PHP programming language and MySQL database, and designed with an intuitive and easy-to-use user interface. Thus, SISKA application can be an effective and efficient solution to improve the quality of academic services at Bumigora University.
Analysis of Tourist Sentiment towards Tourist Attractions in the Mandalika Special Economic Zone Using the Naïve Bayes Method Pribadi, Teguh Iman; Fahry, Fahry; Muharis, Muharis; Marswandi, Ega Dwi Putri
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i1.4081

Abstract

The Mandalika Special Economic Zone has become one of the most popular destinations for both domestic and international tourists. This popularity highlights the importance of understanding the views and feelings of the tourists. Therefore, this study aims to analyze tourist sentiment towards the attractions in the Mandalika Special Economic Zone. The data analyzed was obtained from 1,144 reviews on the TripAdvisor platform. The research stages included data collection, data labeling, data preprocessing, data transformation, data classification, as well as data analysis and visualization. The results of this study indicate that the majority of tourists have a positive sentiment towards the attractions in the Mandalika Special Economic Zone. Furthermore, testing with the Naïve Bayes algorithm successfully classified tourist sentiments accurately, with consistent accuracy rates obtained from each fold: fold 1: 89.08%, fold 2: 89.96%, fold 3: 88.21%, fold 4: 87.34%, and fold 5: 90.79%.
Detection of Rice Diseases Using Leaf Images with Visual Geometric Group (VGG-19) Architecture and Different Optimizers Lalu Zazuli Azhar Mardedi; Fahry Fahry; Miftahul Madani; Hairani Hairani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5286

Abstract

Rice is a major food commodity in Indonesia that plays a vital role in maintaining national food security. However, rice productivity often declines due to pest and disease attacks, especially when the disease is not detected early. Currently, the process of identifying rice diseases is generally still carried out manually by farmers or experts through direct observation, which is subjective, time-consuming, and prone to identification errors. To overcome these limitations, a technology-based solution is needed that is able to detect rice diseases automatically, quickly, and accurately. This study aims to develop a rice disease detection system based on leaf images using a deep learning approach with the Visual Geometric Group (VGG-19) architecture. The research method used is experimental by comparing the performance of the VGG-19 architecture using three different types of optimizers, namely Adaptive Moment Estimation (ADAM), Root Mean Square Propagation (RMSProp), and Stochastic Gradient Descent (SGD), to obtain the best accuracy in rice disease classification. The findings show that the combination of VGG-19 with the ADAM optimizer produces the highest accuracy of 96.45%, followed by RMSProp at 95.96% and SGD at 87.08%. These findings indicate that the selection of optimizers plays an important role in improving the performance of deep learning models, especially in detecting rice diseases based on leaf images.
OPTIMIZING SENTIMENT ANALYSIS OF PRODUCT REVIEWS ON MARKETPLACE USING A COMBINATION OF PREPROCESSING TECHNIQUES, WORD2VEC, AND CONVOLUTIONAL NEURAL NETWORK Fahry, Fahry; Utami, Ema; Sudarmawan, Sudarmawan
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 1 (2023): JUTIF Volume 4, Number 1, February 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.1.815

Abstract

This research attempts to identify the most accurate and effective model in performing sentiment analysis on product reviews in marketplaces using preprocessing techniques, word2vec, and CNN. We collected 20,986 reviews from 720 products in a marketplace using scrap method, then cleaned and labeled the data to include 515 positive reviews, 490 negative reviews. We then performed preprocessing on the data using four different scenarios and identified word vector representation using word2vec. Subsequently, we applied the results of word2vec to the CNN architecture to classify sentiment in product reviews. After trying various variations of each technique, we found that a combination of the third preprocessing technique (case folding, punctuation removal, word normalization, and stemming), the second word2vec parameter combination (size 50, window 2, hs 0, and negative 10), and the fourth CNN parameter combination (kernel size 2, dropout 0.2, and learning rate 0.01) had the best accuracy of 99.00%, precision of 98.96%, and recall of 98.96%. We also found that the word normalization technique greatly helped to increase model accuracy by correcting improperly written or incorrect words in the reviews. Based on the evaluation of word2vec, the hs 0 method produced a higher average accuracy compared to the hs 1 method because the hs 0 method used negative sampling which helped the model understand the context of the trained words. In the CNN parameter, higher learning rates can cause the model to learn faster, but can also cause the model to be unstable, while lower learning rates can make the model more stable but can also cause the model's learning process to be slower.
CODE-SWITCHING PATTERNS ACROSS SASAK DIALECT ZONES: A CORPUS-BASED SOCIOLINGUISTIC ANALYSIS Titik Ceriyani Miswaty; Fahry Fahry
MABASAN Vol. 20 No. 1 (2026): Mabasan 20 (1)
Publisher : Balai Bahasa Nusa Tenggara Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62107/mab.v20i1.1236

Abstract

This study investigates code-switching patterns across different Sasak dialect zones in Lombok Island, employing a corpus-based sociolinguistic approach. The Sasak language, spoken by approximately 3.5 million people in Lombok, exhibits significant dialectal variation distributed across five recognized dialect zones: Ngeno-Ngene, Meno-Mene, Ngeto-Ngete, Kuto-Kute, and Meriak-Meriku. Despite increasing scholarly attention to Sasak dialectology, the phenomenon of code-switching between Sasak dialects and Indonesian—particularly across different dialect zones—remains underexplored. This research addresses two main questions: (1) what are the dominant code-switching patterns in each Sasak dialect zone, and (2) what sociolinguistic factors influence the variation in code-switching behavior across these zones. Data were collected from a geographically-tagged digital corpus comprising 12,480 utterances gathered from 156 speakers across 26 observation points in Lombok. The analysis employed Poplack's (1980) typology of code-switching (tag-switching, inter-sentential, and intra-sentential) combined with Myers-Scotton's (1993) Markedness Model. The findings reveal that inter-sentential switching is the most prevalent type across all dialect zones (48.3%), followed by intra-sentential (34.7%) and tag-switching (17.0%). Significant variation was found across dialect zones, with urban-adjacent zones (Ngeno-Ngene) exhibiting higher rates of intra-sentential switching, while peripheral zones (Meriak-Meriku) showed greater preference for tag-switching. The results suggest that geographical proximity to urban centers, speaker age, and education level are the primary sociolinguistic factors driving code-switching variation across Sasak dialect zones.
Intergenerational Dialect Transmission in Sasak: Evidence from a Geo-Tagged Corpus Titik Ceriyani Miswaty; Fahry Fahry; Wahyu Kamil Syarifaturrahman
Humanitatis : Journal of Language and Literature Vol. 12 No. 2 (2026): Humanitatis: Journal of Language and Literature: In-Press
Publisher : LPPM Universitas Bumigora Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/humanitatis.v12i2.6353

Abstract

The Sasak language of Lombok Island, Indonesia, faces increasing pressure due to urbanization, infrastructure expansion, and growing exposure to national media, raising concerns about intergenerational dialect attrition. Despite growing scholarly attention to Sasak linguistics, no prior study has systematically examined how dialectal features are transmitted across generational cohorts using spatially anchored corpus evidence. This study aims to investigate which phonological, lexical, and morphosyntactic features of Sasak exhibit the highest degree of intergenerational stability, and how geographic location modulates the fidelity of dialect transmission across three generational cohorts. A variationist sociolinguistic approach combined with corpus-based analysis and Geographic Information System (GIS) mapping was employed. Data were collected from 180 native Sasak speakers across 15 geo-tagged villages spanning all five dialect zones, using semi-structured interviews, word-list elicitation, and a dialect perception survey. Acoustic analysis, lexical frequency profiling, and morphosyntactic tagging were conducted using ELAN and Praat. Findings reveal that phonological features consistently exhibit higher intergenerational stability (mean Intergenerational Transmission Index/ITI = 0.78 for Generation 1 to Generation 2; 0.71 for Generation 2 to Generation 3) than lexical features (mean retention = 58.3% for Generation 3). Urban-peripheral zones exhibit accelerated lexical attrition and phonological convergence toward the Ngeno-Ngene prestige dialect, whereas rural-interior communities maintain higher dialectal fidelity. These results underscore the critical role of geographic and social factors in shaping dialect transmission, with implications for language documentation and revitalization in Nusa Tenggara Barat.
Analyzing Marketplace Reviews Using Word2Vec, CNN, and Deep K-Means with Sociolinguistic Approaches Fahry, Fahry; Miswaty, Titik Ceriyani; Harun, Harun
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5340

Abstract

This study investigates the effectiveness of deep learning methods in analyzing linguistically diverse customer reviews on Shopee to generate actionable product insights. By integrating Word2Vec, Convolutional Neural Networks (CNN), and Deep K-Means clustering, the proposed workflow moves beyond simple polarity detection toward aspect-based sentiment analysis. Customer reviews were preprocessed and represented using Word2Vec (skip-gram) to capture semantic proximity across informal registers, slang, abbreviations, and code-switching. A one-dimensional CNN then classified reviews into positive and negative sentiments, achieving 93–94% accuracy with balanced F1-scores across both classes. To extract aspect-level insights, reviews were projected into a latent space via an autoencoder and clustered using K-Means, with evaluation metrics (Silhouette ≈ 0.6; DBI ≈ 0.5) confirming adequate cohesion and separation. Positive clusters highlighted product design, durability, and ease of use, while negative clusters emphasized material quality, packaging, and delivery issues. These findings demonstrate that deep learning can adapt to sociolinguistic variation in Indonesian e-commerce discourse while providing structured, socially meaningful insights. This research is significant for the field of Informatics as it advances Natural Language Processing techniques for multilingual and code-switched data, addressing a key challenge in real-world text mining applications. The approach offers practical value for sellers in improving product quality, enhancing customer satisfaction, and refining marketing strategies.
Co-Authors Adam, M. Awaludin Ega Dwi Putri Marswandi Elisa, Nopa Ema Utami Hairani Hairani Harun Harun Khasnur Hidjah Komala, Rina Lalu Zazuli Azhar Mardedi Miftahul Madani Miswaty, Titik Ceriyani Muhammad Agus Muljanto Muharis, Muharis Ondi Asroni RR. Ella Evrita Hestiandari Sudarmawan, Sudarmawan Teguh Iman Pribadi Wahyu Kamil Syarifaturrahman Widia Febriana Widia Febriana, Widia YASYIFA DIAN URFINA