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All Journal Jurnal EECCIS Jurnal Teknologi Informasi dan Multimedia Conten : Computer and Network Technology INOVTEK Polbeng - Seri Informatika
Feddy Setio Pribadi
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Computer Science & IT Control & Systems Engineering Engineering

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Pemeriksaan Pola Kalimat Otomatis Pada Sebuah Karangan Menggunakan POS Tagging Bahasa Indonesia Dan LALR Parser Sukmandaru Hari Wijayanto; Feddy Setio Pribadi
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 4 No 3 (2022): November
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v4i3.263

Abstract

Dalam era perkembangan teknologi yang pesat ini, berbahasa mempunyai peran penting dalam kehidupan sehari-hari seperti untuk berkomunikasi dengan sesama secara lisan maupun tulisan. Komunikasi akan berlangsung dengan baik jika bahasa yang digunakan dapat dipahami sehingga pesan dapat tersampaikan. Dalam komunikasi tulisan, keterampilan menulis diperlukan untuk menghasilkan tulisan yang dapat menyampaikan pesan dengan baik. Salah satu bentuk hasil dari keterampilan menulis adalah sebuah karangan. Penulisan karangan harus memperhatikan kaidah pemakaian bahasa yaitu fonologi, morfologi, dan sintaksis. Pentingnya kaidah tersebut khususnya sintaksis atau struktur dan pola kalimat dapat mengungkapkan ide yang dapat tersampaikan dengan baik dan mudah untuk dipahami melalui karangan. Penelitian ini bertujuan untuk membantu dalam memeriksa pola kalimat pada sebuah karangan secara otomatis. Dalam pemeriksaan ini diimplementasikan dengan bahasa pemrograman python pada jupyter notebook menggunakan library nltk untuk proses preprocessing, library flair nlp untuk proses part of speech tagging bahasa Indonesia dan penggunaan tabel lalr parser untuk pemeriksaan pola kalimat. Pola kalimat yang digunakan pada pemeriksaan ini adalah S-P, S-P-O, S-P-K, S-P-O-K, S-P-Pel-K, dan S-P-O-Pel-K. Hasil dari penelitian ini adalah berupa pemeriksaan pola kalimat otomatis pada sebuah karangan sederhana dengan batasan menggunakan kalimat tunggal dan kalimat aktif. Pemeriksaan ini dapat memeriksa 14 dari 16 kalimat pada karangan dengan nilai keberhasilan sebesar 87,5% dan nilai keakuratan sebesar 62,5%. Faktor yang mempengaruhi hasil tersebut adalah variasi komponen pola kalimat yang masih terbatas dan penggunaan flair nlp dalam proses pos tagging yang dapat menghasilkan label jenis yang berbeda pada suatu kata yang dipengaruhi oleh letak posisi kata tersebut pada sebuah kalimat.
Systematic Literature Review: Optimizing Broiler Chicken Cage Temperature and Humidity Fidel Lusiana Putri; Deva Kurnia Setiawan; Arsmanda Adi Nugraha; Feddy Setio Pribadi; Rizky Ajie Aprilianto
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 18 No. 3 (2024)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v18i3.1694

Abstract

Broiler production is highly dependent on environmental conditions, especially temperature and humidity in the cage. This research is a Systematic Literature Review that uses the PRISMA method to optimize the temperature and humidity of broiler cages. A total of 202 journals were found through Google Scholar and other repositories, with an additional 16 journals manually. After filtering, 38 journals were selected based on publication year 2019-2024. From the review of the selected literature, five research questions were derived that led to new understanding and solutions to the problems in the research topic. The results show that temperature and humidity significantly impact broiler performance, including health, welfare, and productivity. An IoT-based monitoring and control system is proven to assist with real-time monitoring and control of temperature and humidity, improving farm efficiency and effectiveness. Supporting factors for implementing this system include a friendly user interface, mobile applications, and real-time notifications. The application of IoT technology in broiler farming has the potential to provide significant benefits to farmers and the livestock industry as a whole.
Modelling, Simulation, and Analysis of Sequence-Based Models for Smart Lighting Voice Command Classifiers with MFCC-Based Data Augmentation Yohanes Batara Setya; Feddy Setio Pribadi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/r4p60871

Abstract

Voice command classification is essential for smart lighting systems in IoT environments. However, existing approaches often struggle in real-world scenarios with background noise and speaker variability due to limited and imbalanced training data. This indicates a need for models that maintain high accuracy under such conditions. To address this, the study evaluates three deep learning architectures: a Deep Neural Network (DNN), a Gated Recurrent Unit (GRU), and a bidirectional Long Short-Term Memory (LSTM) network, run on the Google Speech Commands dataset. The classification targets six voice commands (“right”, “off”, “left”, “on”, “down”, “up”) using Mel-Frequency Cepstral Coefficients (MFCCs) as features. Data augmentation techniques, including pitch shifting, time stretching, mix-up, and noise injection, are used to expand the dataset, balance class distributions, and simulate acoustic conditions such as background noise and speaker differences. Model performance is assessed through confusion matrices and receiver operating characteristic curves (ROC-AUC) across training, validation, and test sets. The bidirectional LSTM achieves the highest test accuracy (94%), followed by GRU (92%) and DNN (79%). The LSTM model also generalizes well, showing no signs of overfitting and maintaining stable performance in the presence of acoustic variation. These results suggest that combining bidirectional LSTM with MFCC-based augmentation provides a more robust approach to voice command recognition, particularly in IoT-based smart lighting contexts, where environmental variability is common.
PENGEMBANGAN WEBSITE POINT OF SALES UNTUK GALERI MUTIARA BATIK SOLO Fadila Ainun Zaqi Irmadhani; Naufal Hilmi Fathul Ihsan; Feddy Setio Pribadi; Mukhlas Fajar Putra; Mahen, Mahendra Adiastoro; Febry Putra Rochim; Muhammad Hanif Al Ghifaari; Moh Nafi Adhi Rajasa; Ning Imas Ati Zuhrotal Afifah; Tedwin Arif Muhammad
CONTEN : Computer and Network Technology Vol. 5 No. 1 (2025): Juni 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bcjhzy47

Abstract

Galeri Mutiara Batik Solo merupakan salah satu pelaku usaha mikro yang bergerak di bidang penjualan batik dan kerajinan khas Solo, namun masih menggunakan proses manual dalam pencatatan transaksi, pengelolaan stok, dan pelaporan keuangan. Kondisi ini menyebabkan ketidakefisienan operasional dan keterbatasan dalam pengambilan keputusan berbasis data. Penelitian ini mengusulkan pengembangan sistem Point of Sales (POS) berbasis web dengan pendekatan user-centered design yang didukung framework Laravel. Metode pengembangan menggunakan model waterfall dengan pendekatan pengembangan paralel antar pengembang. Penelitian ini mencakup analisis kode lama, modifikasi dan penambahan fitur-fitur baru seperti manajemen hak akses berbasis peran, sistem pelaporan PDF, keamanan akun, barcode generator, dan penyederhanaan antarmuka. Hasil pengujian menunjukkan sistem POS yang dikembangkan mampu meningkatkan efisiensi kerja, keamanan data, serta kualitas pelayanan pelanggan. Sistem ini juga memiliki potensi untuk dikembangkan lebih lanjut dalam mendukung penjualan omnichannel dan integrasi teknologi mobile maupun cloud untuk UMKM.
A Systematic Literature Review on Machine Learning Techniques for Enhancing Embedded Hardware Reliability Desy Natalia; Cahya Renita Pulse; Rizal Ramadhan; Rama Fahrizal Kusuma; Rizky Ajie Aprilianto; Feddy Setio Pribadi
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Embedded systems (ES) have played a vital role in industrial automation and critical infrastructure, but their reliability has often been compromised by hardware faults, leading to downtime and safety concerns. Traditional threshold-based fault detection methods have frequently failed to adapt to dynamic environments and have struggled to identify early-stage failures. This study reviewed the effectiveness of artificial intelligence (AI), specifically machine learning (ML) models, for fault detection in ES. A systematic review methodology was employed to analyze the diagnostic performance of several deep learning (DL) architectures, including hybrid convolutional neural network-long short-term memory (CNN-LSTM) models, when implemented on resource-constrained edge devices. The results showed that optimized AI models achieved higher diagnostic accuracy and earlier fault identification compared to conventional approaches. Furthermore, these models enabled real-time, energy-efficient operation on platforms such as Raspberry Pi and ESP32 microcontrollers. It was concluded that AI-driven solutions significantly enhanced predictive maintenance and operational reliability in embedded system applications, demonstrating their transformative potential for future industrial systems.
Co-Authors Arsmanda Adi Nugraha Cahya Renita Pulse Desy Natalia Deva Kurnia Setiawan Fadila Ainun Zaqi Irmadhani Fidel Lusiana Putri Mahen, Mahendra Adiastoro Moh Nafi Adhi Rajasa Muhammad Hanif Al Ghifaari Mukhlas Fajar Putra Naufal Hilmi Fathul Ihsan Ning Imas Ati Zuhrotal Afifah Rama Fahrizal Kusuma Rizal Ramadhan Rizky Ajie Aprilianto Rochim, Febry Putra Sukmandaru Hari Wijayanto Tedwin Arif Muhammad Yohanes Batara Setya