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All Journal TEKNIK INFORMATIKA Jurnal Teknologi Informasi dan Ilmu Komputer Fundamental Management Journal Jurnal Informatika Universitas Pamulang MULTINETICS Building of Informatics, Technology and Science Jurnal E-Komtek Journal of Applied Data Sciences fundamental management journal
Nenny Anggraini, Nenny
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Journal : TEKNIK INFORMATIKA

 1 
Implementation of IoT Technology on MySmartTrash Waste Bank Application Viva Arifin; Siti Ummi Masruroh; Rizka Amalia Putri; Fitri Mintarsih; Nenny Anggraini; Nurhayati; Dewi Khairani
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.v18i2.46673

Abstract

The effectiveness of Waste Bank in addressing national waste management challenges is limited by inherent shortcomings. Conventional processes, which heavily rely on manual labor and record-keeping, often face logistical challenges and inefficiencies that limit the effectiveness of waste banks. This paper presents the MySmartTrash application, a solution that integrates IoT technology to enhance waste management practices through a smart waste bank system. By utilizing IoT-enabled sensors, the application allows users to monitor waste levels in real time, thereby optimizing waste collection processes and promoting effective waste segregation. This study employed IoT Design Methodology and Prototyping. Through a SWOT analysis of existing waste management applications, the research identifies strengths and opportunities for enhancing waste management systems. Usability testing also highlighted the significance of various features. This study offers insights for future research into IoT applications in environmental sustainability and waste management systems.
A Comparative Analysis of Random Forest, XGBoost, and LightGBM Algorithms for Emotion Classification in Reddit Comments Anggraini, Nenny; Putra, Syopiansyah Jaya; Wardhani, Luh Kesuma; Arif, Farid Dhiya Ul; Hakiem, Nashrul; Shofi, Imam Marzuki
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: 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.v17i1.38651

Abstract

This research aims to compare the performance of three classification algorithms, namely Random Forest, XGBoost, and LightGBM, in classifying emotions in Reddit comments. Emotion classification in Reddit comments is a complex classification problem due to its numerous variations and ambiguities. This research utilizes the GoEmotions Fine-Grained dataset, filtered down to 7,325 Reddit comments with 5 different basic emotion labels. In this study, data preprocessing steps, feature extraction using CountVectorizer and TF-IDF, and hyperparameter tuning using GridSearchCV for each algorithm are conducted. Subsequently, model evaluation is performed using Cross-Validation and confusion matrix. The results of the study indicate that Random Forest outperforms the XGBoost and LightGBM algorithm with an accuracy of 75.38% compared to XGBoost with 69.05% accuracy and LightGBM with 66.63% accuracy.
Impact of Wavelet Denoising on LSTM-Based Greeting Sentence Recognition Using the IndSpeech Teldialog SVCR Dataset Shabira Zhillan; Wardhani, Luh Kesuma; Anggraini, Nenny; Nashrul Hakiem; Imam Marzuki Shofi
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.49040

Abstract

Speech signals play a crucial role in human communication, particularly in speech recognition systems. However, speech recognition performance is often compromised by noise in the audio signal. This study aims to examine the effect of wavelet denoising technique on greeting sentence data containing artificial white noise before performing speech recognition using Long Short-Term Memory (LSTM). Mel Frequency Cepstral Coefficient (MFCC) is used as speech feature extraction. The results show that speech recognition accuracy reaches 90% on clean data. Accuracy drops to 51% when tested on data with noise, indicating a significant decrease of 39 percentage points. After applying the wavelet denoising method, accuracy improved using the two best parameter combinations. The combination with the highest SNR value resulted in an improvement of 18 percentage points, while the combination with the highest PESQ value resulted in an improvement of 13 percentage points. These findings indicate that the wavelet denoising method is capable of improving the performance of LSTM-based speech recognition in noisy environments.
Co-Authors 1, Aprisani AA Sudharmawan, AA Achmad Nizar Hidayanto Arif, Farid Dhiya Ul Ariq Cahya Wardhana Assyahid, Muhammad Hudzaifah Br Sihaloho, Ruth Devi Yuliani Dwi Cahyanti, Dwi Fitri Mintarsih Heni Pujiastuti Husni Teja Sukmana Hutapea, Ganda T. Khairani, Dewi Ktut Silvanita Luh Kesuma Wardhani Lumbantoruan, Rutman Marchelina, Eva Muhammad Yusuf Muhammad Yusuf Nurpariz Naiborhu , Joseph Rivan Nashrul Hakiem Nurhayati Panjaitan, Robert Rizka Amalia Putri Rozy, Nurul Faizah Sari, Putu Indah Mariska Sembiring, Carolina F. Setyawan, Okky Bagus Shabira Zhillan Shaeku, Muhammad Akhil Shofi, Imam Marzuki Shofi, Imam Marzuki Simamora, Engelina Lawita Siti Ummi Masruroh Syopiansyah Jaya Putra Tobing, Josephine Viva Arifin Yusuf Rahman