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All Journal Jurnal Ilmiah KOMPUTASI Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Journal of Computer System and Informatics (JoSYC) JIKI (Jurnal Ilmu Komputer dan Informatika) Media Akuntansi Perpajakan
Wahyuni, Diny
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Social Sciences

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Long Short-Term Memory dan Lexicon Based Untuk Analisis Sentimen Ulasan Aplikasi TikTok Wahyuni, Diny; Fadhillah, Naufal; Ariestya, Winda Widya
Jurnal Ilmiah Komputasi Vol. 23 No. 2 (2024): Jurnal Ilmiah Komputasi : Vol. 23 No 2, Juni 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.23.2.3579

Abstract

Aplikasi TikTok menjadi salah satu aplikasi yang paling banyak di unduh yaitu lebih dari 1 miliar unduhan pada Google Playstore. Sebuah analisis sentimen diperlukan untuk mengetahui opini pengguna mengenai kepuasan pengguna dalam menggunakan aplikasi TikTok. Tahapan proses analisis sentimen dimulai dengan pengambilan data (crawling), text pre-processing, klasifikasi sentimen serta penyusunan hasil analisis sentimen. Hasil dari tahap text pre-processing yang diperoleh, dilakukan penentuan sentimen awal dengan menggunakan metode Lexicon Based dengan perhitungan otomatis. Selanjutnya dilakukan pembagian data menjadi data training dan data testing untuk mendapatkan model yang optimal dan memprediksi sentimen dengan model Long Short-Term Memory (LSTM). Pada penelitian ini ulasan dari sistem analisis sentimen dengan metode LSTM akurasi yang didapatkan sebesar 90,05%, precision 92,14%, recall 97,35%, dan F-1 Score 98,66%, ulasan positif 30,0%, ulasan negatif 59,5%, dan ulasan netral 10,5%. Hasil analisis sentimen pada penelitian ini menunjukkan bahwa aplikasi TikTok memiliki kecenderungan sentimen negatif pada saat data ulasan diambil.
Penerapan Algoritma K-Medoids Data Mining untuk Clustering Wilayah Penderita Demam Berdarah Berdasarkan Data Set Wahyuni, Diny
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5831

Abstract

Disease is a disorder that occurs in the body, either in form or function, so that the body cannot work properly or normally. Dengue fever (DF) is an infection caused by the dengue virus that can cause accurate fever. Dengue fever is still a serious problem for public health. The Health Service in each region has the task of helping the community in dealing with dengue fever cases. Data sets are collections of data arranged in a structured format, such as tables or files, and contain information from various sources. In this study, data mining analysis was carried out using the Clustering technique using the K-Medoids method. The use of the K-Medoids Algorithm is said to be better at grouping datasets than k-means because K-Medoids is one of the effective clustering methods for dealing with small datasets. Data mining can be interpreted as the process of selection, exploration, and modeling of large amounts of data to find patterns or tendencies that are usually not realized. Clustering is a process of grouping records, observations, or grouping classes that have similar objects. The results obtained from the study show that the application of the K-Medoids algorithm can be done to form 2 clusters. In the first cluster there are 4 cluster results and in the second cluster there are 6 cluster results.
REAKSI MEDIA SOSIAL TERHADAP KONFLIK ISRAEL-PALESTINA: PENDEKATAN ANALISIS SENTIMEN DENGAN VADER DAN BERT Ariestya, Winda Widya; Wahyuni, Diny; Winarko, Ananda Rizky
JIKI (Jurnal llmu Komputer & lnformatika) Vol. 5 No. 2 (2024): JIKI Desember 2024
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jiki.v5i2.7420

Abstract

Konflik Israel-Palestina merupakan salah satu isu geopolitik yang paling sensitif dan berlarut-larut dalam sejarah modern, menarik perhatian masyarakat internasional serta memicu berbagai reaksi di media sosial. Twitter, sebagai salah satu platform utama untuk diskusi publik, mencerminkan opini dan sikap global terhadap isu ini. Penelitian ini bertujuan untuk menganalisis reaksi pengguna Twitter terhadap konflik Israel-Palestina melalui pendekatan analisis sentimen, menggunakan dua model yaitu VADER (Valence Aware Dictionary and Sentiment Reasoner) dan BERT (Bidirectional Encoder Representations from Transformers). Pengujian menghasilkan akurasi sebesar 92% dan menunjukan sentimen positif terhadap Palestina sebagai sentimen yang dominan.
DETEKSI DINI KARDIOMEGALI MENGGUNAKAN KLASIFIKASI DEEP LEARNING BERBASIS CONVOLUTIONAL NEURAL NETWORK PADA CITRA X-RAY DADA Wahyuni, Diny; Waskito, Bahtiar; Ariestya, Winda Widya; Astuti, Ida; Ruhama, Syamsi
JIKI (Jurnal Ilmu Komputer dan Informatika) Vol. 6 No. 1 (2025): JIKI (Jurnal Ilmu Komputer dan Informatika) Juli 2025
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jiki.v6i1.7643

Abstract

Proses diagnosa kardiomegali pada dada membutuhkan kecepatan dan akurasi tinggi, namun metode manual seperti wawancara pasien dan analisis subjektif rontgen sering menyebabkan hasil kurang akurat dan perbedaan persepsi antar dokter. Untuk mengatasi hal tersebut, penelitian ini mengembangkan klasifikasi kardiomegali menggunakan teknologi Deep Learning berbasis Convolutional Neural Network (CNN). Penelitian melibatkan dua tahap utama dalam pembuatan model, yaitu Feature Extraction dan Classification, menggunakan 5600 data (4000 untuk pelatihan, 1000 untuk pengujian, dan 600 untuk validasi). Setelah delapan percobaan, akurasi tertinggi diperoleh pada percobaan menggunakan batch size 50, epoch 10 dengan hasil sebesar 95,59%. Evaluasi menggunakan Confusion Matrix, diperoleh accuracy 93%, precision untuk kardiomegali 97%, serta recall sebesar 89%. Hasil ini menunjukkan bahwa metode CNN efektif untuk membantu diagnosa kardiomegali menjadi lebih cepat dan akurat.
Deep Learning for Horticulture: Convolutional Neural Network Driven Classification of Banana Types Astuti, Ida; Ibrahim, Lutfi Nabhan; Ariestya, Winda Widya; Ruhama, Syamsi; Wahyuni, Diny
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 16 No. 1 (2025): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v16i1.23812

Abstract

One of the most widely grown horticulture fruits in Indonesia is the banana. In addition to its various health benefits, bananas are a good source of carbohydrates and vitamins A, C, and E. There are a lot of different kinds of bananas in Indonesia, and occasionally people have trouble telling them apart. This study uses a Convolutional Neural Network (CNN), a Deep Learning technique, to categorize bananas. Four different types of bananas—Cavendish, Kepok, Raja, and Tanduk—were classified. Planning, analysis, creating a banana classification model with CNN, and assessing the outcomes are the four phases of the research process. Data preprocessing, CNN model creation, training, and testing procedures are the next steps in the categorization model design process, which starts with the collection of banana data using a smartphone camera. The optimal model was obtained with the accuracy value of 96%, the average precision and recall values of 97% and 96% respectively. It was found based on test results with multiple tuning parameters, including dataset partition, optimizer use, and epoch. This study offers novelty in terms of the use of a large banana image dataset, extensive exploration of CNN parameters, and the potential application of the model in applications for the horticultural industry. In addition, this study contributes to the development of image-based AI technology in agricultural product classification, which is still relatively underexplored in Indonesia
Analisis Penerapan Biaya Lingkungan Dalam Kaitan Pelaporan Akuntansi Pada PLTMG 30 MW Baubau Wahyuni, Diny; Abdullah, Muntu; Asni, Nur
Media Akuntansi Perpajakan Vol 10, No 2 (2025): Media Akuntansi Perpajakan
Publisher : Universitas 17 Agustus 1945 Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52447/map.v10i2.8864

Abstract

This study aims to analyze the implementation of environmental cost accounting at the 30 MW Baubau Gas Engine Power Plant (PLTMG) through an assessment of five main stages: identification, recognition, measurement, presentation, and disclosure of environmental costs. This study employs a descriptive qualitative approach, encompassing primary and secondary data obtained through interviews, observations, and documentation. The results indicate that the implementation of environmental cost accounting at the 30 MW Baubau PLTMG reflects the company's commitment to sustainable environmental management. Environmental costs have been identified through a dedicated account named "Environmental and Occupational Health and Safety Costs." Although the recognition, measurement, and presentation stages have been conducted in accordance with PSAK Number 201 of 2024, disclosure in the Notes to Financial Statements remains limited and does not yet provide detailed information regarding environmental costs.
Co-Authors Chatarina Umbul Wahyuni Fadhillah, Naufal Ibrahim, Lutfi Nabhan Muntu Abdullah, Muntu Nur Asni Ruhama, Syamsi Waskito, Bahtiar Winarko, Ananda Rizky Winda Widya Ariestya