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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA Jurnal Informatika Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi AMIKOM ICT AWARD 2010 Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Buana Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Dinamika Informatika CommIT (Communication & Information Technology) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal technoscientia Jurnal Teknologi Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) JUITA : Jurnal Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Seminar Nasional Informatika (SEMNASIF) ELINVO (Electronics, Informatics, and Vocational Education) CESS (Journal of Computer Engineering, System and Science) Proceeding SENDI_U Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Jurnal Inspiration KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Proceeding of the Electrical Engineering Computer Science and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Creative Information Technology Journal AT-Tahdzib: Jurnal Studi Islam dan Muamalah SISFOTENIKA IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Ilmiah Universitas Batanghari Jambi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal InComTech: Jurnal Telekomunikasi dan Komputer Insect (Informatics and Security) : Jurnal Teknik Informatika Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Komtika (Komputasi dan Informatika) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang Applied Information System and Management Jurnal Sinergitas PkM & CSR Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah RESEARCH : Computer, Information System & Technology Management INTECOMS: Journal of Information Technology and Computer Science JurTI (JURNAL TEKNOLOGI INFORMASI) Angkasa: Jurnal Ilmiah Bidang Teknologi Jiko (Jurnal Informatika dan komputer) CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) Journal on Education JURTEKSI Jurnal Informasi dan Komputer Multitek Indonesia : Jurnal Ilmiah Jurnal Manajemen Informatika Jambura Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EXPLORE ComTech: Computer, Mathematics and Engineering Applications CSRID (Computer Science Research and Its Development Journal) Jurnal Ilmiah Sinus Informasi Interaktif Majalah Ilmiah Bahari Jogja CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika TAFAQQUH: Jurnal Hukum Ekonomi Syariah Dan Ahwal Syahsiyah Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah JURNAL TAHURI SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Aisyah Journal of Informatics and Electrical Engineering Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics TAJDID KURVATEK Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Tecnoscienza Respati Jurnal Teknika IT (INFORMATIC TECHNIQUE) JOURNAL JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Intelligent Decision Support System (IDSS) G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems Jurnal Sistem Komputer dan Informatika (JSON) Journal of Innovation Information Technology and Application (JINITA) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Bulletin of Computer Science and Electrical Engineering (BCSEE) Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) TEPIAN Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Mitra Mahajana: Jurnal Pengabdian Masyarakat Journal of Applied Computer Science and Technology (JACOST) Jurnal Pendidikan dan Teknologi Indonesia International Journal of Computer and Information System (IJCIS) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) International Research on Big-data and Computer Technology (IRobot) Bulletin of Computer Science Research INFOSYS (INFORMATION SYSTEM) JOURNAL J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Jurnal Ekonomi dan Teknik Informatika International Journal Artificial Intelligent and Informatics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Jurnal Nasional Teknik Elektro dan Teknologi Informasi sudo Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram EXPLORE Journal of Comprehensive Science Techno SENTRI: Jurnal Riset Ilmiah Indonesian Journal Computer Science (ijcs) Jurnal Educative: Journal of Educational Studies Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer JURNAL TEKNIK INDUSTRI STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Pendidikan Indonesia (Japendi) Cerdika: Jurnal Ilmiah Indonesia International Journal of Advanced Science Computing and Engineering Innovative: Journal Of Social Science Research J-Icon : Jurnal Komputer dan Informatika Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Teknomatika: Jurnal Informatika dan Komputer Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Tafaqquh : Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah Explore The Indonesian Journal of Computer Science Scientific Journal of Informatics Jurnal Teknologi KOPEMAS International Journal of Information Engineering and Science At-Tahdzib: Jurnal Studi Islam dan Muamalah semanTIK JESICA Jurnal Sistem Informasi Komputer dan Teknologi Informasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Business, Social, Management, and Technology Jurnal Komtika (Komputasi dan Informatika)
Ema Utami
Universitas Amikom Yogyakarta
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Hybrid Fuzzy Logic, Genetic Algorithms, and Artificial Neural Networks for Cattle Body Weight Prediction Anjar Setiawan; Ema Utami; Dhani Ariatmanto
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1319

Abstract

Cattle serve as the primary means of meat and milk production in numerous regions across the globe. Enhancing efficiency and productivity in cow ranching can provide significant economic consequences. The cattle industry is significant as it enables the estimation of cow weight, directly influencing beef and milk quality. This study aims to enhance the accuracy of cattle weight estimation by minimizing the Mean Squared Error (MSE) values. The integration of artificial neural network (ANN), fuzzy logic (FL), and genetic algorithm (GA) techniques is a promising artificial intelligence tool for predicting and modeling cattle weight in livestock weight prediction systems. The cow weight forecast yielded a Mean Squared Error (MSE) value of 10.9 kg, which is the best result. The results demonstrate the progress made in agriculture using advanced technologies. They offer a detailed examination of how artificial intelligence, fuzzy logic, and evolutionary techniques can be combined to address the many difficulties associated with estimating cattle body weight.
Deep Learning Models For Youtube Sentiment Analysis: A Comparative Study Of Bert And Gru In Danantara Indonesia Rizy, M. Alfa; Utami, Ema
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.12064

Abstract

This study aims to analyse public sentiment toward Danantara Indonesia through YouTube comments and compare the performance of two deep learning models, BERT and GRU, in sentiment classification. The dataset consists of 1,065 comments collected through scraping techniques, which were pre-processed and classified into three sentiment categories: positive, neutral, and negative. The results indicate that the IndoBERT model achieved 100% accuracy, with perfect precision, recall, and F1-score for all sentiment classes. In contrast, the GRU model only achieved 60% accuracy, showing a tendency to classify almost all comments as negative sentiment. Sentiment distribution analysis reveals that the majority of comments (60%) express negative sentiment, followed by positive sentiment (25%) and neutral sentiment (15%). The dominance of negative sentiment suggests public distrust and criticism regarding the policies and transparency of Danantara Indonesia. These findings demonstrate that BERT is a more accurate model for sentiment analysis of Indonesian-language texts. Furthermore, this study recommends further evaluation to address data imbalance and improve the model’s generalization across various social contexts in digital media.
Prediksi Harga Bitcoin Menggunakan Model Hibrida Transformer Dan Long Short Term Memory Irawan, Rio; Utami, Ema
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1091

Abstract

Peramalan harga Bitcoin merupakan tantangan signifikan akibat fluktuasi pasar yang tinggi, di mana model deret waktu konvensional seringkali gagal menangkap dependensi data jangka pendek dan panjang secara bersamaan. Untuk mengatasi masalah tersebut, penelitian ini bertujuan mengembangkan dan mengevaluasi model deep learning hibrida guna meningkatkan presisi peramalan harga Bitcoin. Model yang diusulkan mengintegrasikan arsitektur Long Short-Term Memory (LSTM), yang andal dalam memproses data sekuensial, dengan mekanisme atensi pada Transformer yang mampu mengidentifikasi hubungan data yang relevan. Metode penelitian mencakup penggunaan data harga historis Bitcoin yang telah melalui tahap pra-pemrosesan dan normalisasi. Arsitektur model terdiri dari dua lapisan LSTM (30 unit), diikuti oleh lapisan Transformer dengan Multi-Head Attention (satu head, dimensi kunci empat), dan diakhiri dengan lapisan Global Average Pooling. Pelatihan model dilakukan menggunakan konfigurasi optimal selama 10 epoch dengan ukuran batch 64. Hasil evaluasi menunjukkan kinerja prediksi yang sangat akurat, dibuktikan dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 0,000695 dan R-squared (R²) 0,99999. Temuan ini menegaskan bahwa pendekatan hibrida efektif dalam menangkap pola kompleks pada data harga Bitcoin, sehingga menawarkan alat yang lebih kuat untuk analisis keuangan di pasar cryptocurrency.
DEGREE: Development and Validation of a User Experience Model for Digital Educational Games Using Cronbach’s Alpha and Fuzzy Logic Kurniawan, Mei Parwanto; Suyanto, M.; Utami, Ema; Kusrini, Kusrini
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4942

Abstract

The rapid growth of digital educational games demands an evaluation model that accurately captures user experience and adopts a human-centred approach. This study introduces DEGREE (Digital Educational Game Review and Evaluation Engine), an enhanced model extending MEEGA+ by incorporating two previously underrepresented dimensions: Control and Feedback. Using a quantitative approach, questionnaires were distributed to high school students who actively use Minecraft and Duolingo, yielding 4800 responses.Reliability analysis via Cronbach’s Alpha revealed that the Player Experience + Control combination achieved the highest score (α = 0.914), while the inclusion of Feedback reduced reliability (α = 0.864), leading to its exclusion in the final model. The DEGREE model consists of two core domains: Usability (Aesthetics, Learnability, Operability, Accessibility) and Player Experience (Focused Attention, Fun, Challenge, Social Interaction, Confidence, Relevance, Satisfaction, Perceived Learning, User Error Protection, Control). Evaluation scores were calculated using the Fuzzy Weighted Average (FWA) method and Mean of Maximum (MoM) defuzzification. The Control dimension emerged as the most influential (0.2735), followed by Fun (0.2664) and Satisfaction (0.2516), highlighting the significance of user agency in digital learning environments. The DEGREE model offers a statistically robust and user-oriented framework for evaluating educational games, delivering actionable insights for developers and educators to design more effective and engaging digital learning experiences. This study contributes a new validated and generalizable evaluation framework that strengthens the theoretical foundation of user experience assessment in educational game design.
DEVELOPMENT RICE PLANT DISEASE CLASSIFICATION USING CNN WITH TRANSFER LEARNING Fitrony, Fachri Ayudi; Utami, Ema
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4159

Abstract

Abstract: The rice plant, Oryza sativa, is a major food source in Indonesia. This plant is processed into rice, a staple food for the Indonesian people. Rice growth is crucial to ensure the rice produced is of good quality. One part of the rice plant that is susceptible to disease is the leaves, which can inhibit growth and reduce rice quality. Therefore, early detection and accurate classification of rice diseases are crucial to minimize these negative impacts. This has driven the development of a Deep Learning model capable of high-performance automatic classification. This study aims to create a rice leaf classification model using the CNN algorithm and several transfer learning architectures such as ResNet101, VGG16, and Xception. A dataset of 859 rice leaf images collected from the Kaggle website was then processed using augmentation techniques to a total of 2,439 images, plus 215 smartphone photos for external data validation. Thus, the total dataset increased to 2,656 images, covering four categories: leafblast, brownspot, healthy, and hispa. The model was processed in two stages: on the initial dataset (Non-Augmented Dataset) and the Augmented Dataset. The best experimental results were obtained using the ResNet architecture, with a training accuracy of 96.17% and a validation accuracy of 95.22%. Based on the research results, the rice plant disease classification model using deep learning demonstrated good performance. Keywords: convolutional neural network; deep learning; fine-tuning; image classification; resnet; rice plant
PERBANDINGAN ALGORITMA SVM DAN RANDOM FOREST PADA KLASIFIKASI KESEHATAH MENTAL BERDASARKAN SLEEP DISORDERS Pulungan, Linda Nurul Taqwa; Utami, Ema
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.7392

Abstract

Kesehatan mental semakin menjadi isu global yang mendesak, dengan meningkatnya prevalensi gangguan mental yang mempengaruhi jutaan individu di seluruh dunia. Penelitian ini bertujuan untuk mengevaluasi kinerja dua algoritma pembelajaran mesin, yaitu Support Vector Machine (SVM) dan Random Forest, dalam mendeteksi gangguan kesehatan mental melalui analisis pola tidur. Data yang digunakan berasal dari dataset "Stress Level Detection" di Kaggle, yang telah mengalami augmentasi menjadi 1.375 sampel. Dataset dibagi menjadi 80% untuk pelatihan model dan 20% untuk pengujian. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki kinerja yang lebih baik dibandingkan SVM, dengan akurasi mencapai 91% dan F1-score rata-rata 89%. Sementara itu, SVM memperoleh akurasi 83% dan F1-score rata-rata 81%. Secara spesifik, Random Forest lebih efektif dalam mendeteksi pola tidur normal serta gangguan seperti insomnia. Temuan ini menunjukkan potensi pembelajaran mesin, terutama Random Forest, sebagai alat yang efektif untuk deteksi dini gangguan kesehatan mental melalui analisis pola tidur, yang dapat digunakan untuk mendukung diagnosis lebih cepat dan akurat.
Analisis Sentimen Pada Kurikulum Merdeka Menggunakan Word2Vec Dan Algoritma Long Short-Term Memory Alin, Octhavia; Utami, Ema
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6516

Abstract

Kurikulum Merdeka, sebagai penyempurnaan dari Kurikulum 2013, telah memicu berbagai opini publik sejak diterapkan pada Tahun Ajaran 2022/2023. Opini-opini ini, yang dibagikan melalui platform media sosial seperti Twitter dan YouTube, mencerminkan pandangan beragam dari masyarakat. Penelitian ini menganalisis sentimen terhadap Kurikulum Merdeka menggunakan algoritma LSTM yang dipadukan dengan Word2Vec. Dataset yang digunakan terdiri dari 23.577 teks dari Twitter dan YouTube, yang dikategorikan menjadi sentimen negatif, positif, dan netral. Penelitian ini membandingkan dua arsitektur Word2Vec. Skip-gram dan Continuous Bag of Words (CBOW), serta mengeksplorasi pengaruh optimizer (Adam dan RMSprop) dan learning rate terhadap kinerja model. Hasil penelitian menunjukkan bahwa arsitektur CBOW, yang dikombinasikan dengan optimizer Adam dan learning rate 0.001, memberikan hasil yang paling akurat dan stabil, dengan akurasi mencapai 97% dan tingkat loss yang rendah. Skip-Gram lebih sensitif terhadap perubahan parameter, yang menyebabkan ketidakstabilan kinerja. Penelitian ini menegaskan efektivitas kombinasi Word2Vec dan LSTM untuk analisis sentimen, dengan CBOW dan optimizer Adam sebagai konfigurasi yang paling optimal
Evaluation of E-Learning Usability Based on ISO 25010 with Hofstede's Cultural Dimensions as Moderation: A PLS-SEM Study in Higher Education Januhari, Ni Nyoman Utami; Setyanto, Arief; Kusrini, Kusrini; Utami, Ema; Béjar, Rodrigo Martínez
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45738

Abstract

Although e-learning has rapidly advanced in higher education, many platforms still fall short of meeting user needs due to a lack of integration between usability and cultural dimensions. This study explores how usability influences user satisfaction with e-learning platforms, with cultural dimensions based on Hofstede’s model examined as moderating variables. Usability Quality (QiU) is assessed using the ISO/IEC 25010 framework, which includes five key elements: effectiveness, efficiency, user satisfaction, risk avoidance, and contextual relevance. A total of 384 students from private universities in Bali participated in the study, representing a diverse range of academic disciplines. Using SmartPLS and Partial Least Squares Structural Equation Modeling (PLS-SEM), the analysis revealed that usability has a significant effect on user satisfaction (T=7.528, β=0.270), and cultural variables also play a substantial role (T=21.094, β=0.704). Although the moderating effect of culture was statistically significant (T=2.379, β=0.042), its impact was relatively modest compared to the direct effect of usability. Among the usability components, efficiency emerged as the most influential factor. Regarding cultural dimensions, individualism versus collectivism was found to have the strongest effect. These findings emphasize the importance of designing e-learning systems that are both usability-driven and culturally sensitive, ensuring alignment with user expectations and the educational context.  
PENGARUH KEPEMILIKAN PUBLIK DAN KEPEMILIKAN INSTITUSIONAL TERHADAP PENGHINDARAN PAJAK PADA PERUSAHAAN FNB YANG TERDAFTAR DI BEI PERIODE 2015–2019: THE EFFECT OF PUBLIC OWNERSHIP AND INSTITUTIONAL OWNERSHIP ON TAX AVOIDANCE IN FNB COMPANIES LISTED ON THE IDX IN THE 2015–2019 PERIOD Ema Utami; Sri Wahyuni
Journal of Business, Social, Management, and Technology Vol. 1 No. 1 (2025): June 2025
Publisher : CV. Dhanesa Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64195/j-bsmtech.v1i1.3

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh kepemilikan publik dan kepemilikan institusional terhadap penghindaran pajak pada perusahaan sektor Food and Beverages yang terdaftar di Bursa Efek Indonesia (BEI) selama periode 2015–2019. Metode pengambilan sampel yang digunakan dalam penelitian ini adalah purposive sampling, dengan kriteria laporan keuangan yang memuat data-data yang dibutuhkan dan berakhir setiap 31 Desember, serta memiliki laba bersih negatif. Yang kemudian menghasilkan 65 laporan keuangan tahunan dari perusahaan Food and Beverages yang memenuhi syarat selama lima tahun berturut-turut. Data dianalisis menggunakan metode Partial Least Squares (PLS) melalui bantuan software WarpPLS versi 7.0. Hasil analisis menunjukkan bahwa baik kepemilikan publik maupun kepemilikan institusional memiliki pengaruh negatif terhadap penghindaran pajak. Artinya, semakin besar proporsi kepemilikan publik dan institusional dalam suatu perusahaan, maka semakin kecil kecenderungan perusahaan tersebut untuk melakukan penghindaran pajak. Temuan ini menunjukkan bahwa keterlibatan pemilik saham publik dan institusi dapat meningkatkan pengawasan terhadap manajemen perusahaan, sehingga mendorong kepatuhan perpajakan. Penelitian ini memberikan implikasi penting bagi regulator dan investor, khususnya dalam meningkatkan transparansi dan tata kelola perusahaan yang baik melalui struktur kepemilikan yang sehat.
VARFIS: A Hybrid Neuro-Fuzzy Model for Intelligent Microclimate Control in Black Soldier Fly Farming Systems Yunita Sartika Sari; Kusrini; Ema Utami; Ferry Wahyu Wibowo
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46610

Abstract

Maintaining optimal microclimate conditions is essential for Black Soldier Fly (BSF) cultivation, yet traditional systems often struggle with dynamic environmental changes. This study proposes the Vector Autoregressive-Fuzzy Inference System (VARFIS), a hybrid model combining Vector Autoregression (VAR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to enhance temperature and humidity control in BSF insectariums. VARFIS adapts to uncertainty using probabilistic learning, achieving a 48% reduction in prediction error (MAPE = 1.36%) and high accuracy (R² = 0.9695), outperforming standalone VAR and ANFIS models. The model effectively captures daily climate fluctuations, improving larval growth efficiency and waste conversion. However, it remains limited in handling extreme events such as sudden heatwaves or humidity spikes, indicating the need for enhancements like adaptive fuzzy rule tuning and integration of physical constraints. VARFIS presents a scalable solution for intelligent microclimate management, supporting sustainable insect farming and circular economy goals. This work contributes to precision agriculture by offering data-driven tools for resilient environmental control.
Co-Authors , Anggit Dwi Hartanto A.A. 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