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Monitoring Gadget Usage Behavior Among Adolescents Using Machine Learning Iskandar, Yanti Rubiyanti; Purwarianti, Ayu; Lestari, Dessi Puji; Hendradjaja, Bayu
GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Vol 8, No 2 (2018)
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/gdn.v8i2.1633

The aim study has a long-term goal, namely to reduce negative impact of gadget use among adolescents. By giving awareness and the ability for teens to control the use of gadgets, adolescents are expected will be more productive and act as users of information technology intelligent. From economic products, the software developed can be marketed to various educational institutions such as junior high school or university, or where parents or schools will get a monitoring report on the use of gadgets from adolescents users. The method used in this study includes artificial intelligence techniques (machine learning) for various development models of text/speech / video/type classification user; User Centered Design techniques for application development; and multiple techniques social humanities such as desk study activities, focus group discussions, survey/questionnaire/interview. The results of the first year research to date are software development to monitor user behavior on the gadget, collecting user behavior data adolescents on gadgets, interviewing gadget use on teenage respondents, development. The hate learning model based on deep learning, the development of the rude classification model words based on deep learning and the development of Indonesian parsers.

Shared-hidden-layer Deep Neural Network for Under-resourced Language the Content Devin Hoesen; Dessi Puji Lestari; Dwi Hendratmo Widyantoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7984

Training speech recognizer with under-resourced language data still proves difficult. Indonesian language is considered under-resourced because the lack of a standard speech corpus, text corpus, and dictionary. In this research, the efficacy of augmenting limited Indonesian speech training data with highly-resourced-language training data, such as English, to train Indonesian speech recognizer was analyzed. The training was performed in form of shared-hidden-layer deep-neural-network (SHL-DNN) training. An SHL-DNN has language-independent hidden layers and can be pre-trained and trained using multilingual training data without any difference with a monolingual deep neural network. The SHL-DNN using Indonesian and English speech training data proved effective for decreasing word error rate (WER) in decoding Indonesian dictated-speech by achieving 3.82% absolute decrease compared to a monolingual Indonesian hidden Markov model using Gaussian mixture model emission (GMM-HMM). The case was confirmed when the SHL-DNN was also employed to decode Indonesian spontaneous-speech by achieving 4.19% absolute WER decrease.

Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi Ranny Ranny; Iping Supriana Suwardi; Tati Latifah Erawati Rajab; Dessi Puji Lestari
JUITA : Jurnal Informatika JUITA VoL. 7 Nomor 1, Mei 2019
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Hasil dari peneitian banyak digunakan dan dikembangkan pada aplikasi yang telah banyak dimanfaatkan pada kehidupan sehari-hari. Proses identifikasi bunyi menjadi salah satu penelitian yang banyak dilakukan. Identifikasi bunyi yang dilakukan oleh manusia berbeda satu sama lain. Misal pada suara detak jantung, pada pendengar umum, suara detak jantung tidak memiliki informasi apa pun terkait kesehatan, tapi jika suara detak jantung diperdengarkan pada ahli medik atau dokter, maka informasi yang diperoleh akan berbeda, dokter dapat mengidentifikasikan suara detak jantung dikaitkan dengan kondisi kesehatan jantung. Selain dalam bidang medis, bunyi juga dimanfaatkan pada aplikasi berbasis bunyi dan suara pada Smart Homes. Namun, sebelum mengkaji tentang aplikasi pada Smart Homes dan aplikasi lain maka akan dibahas beberapa teori dasar tentang bunyi dan suara, seperti: teori suara dan bunyi, noise pada data suara, serta ekstraksi ciri suara bunyi yang secara spesifik akan menjelaskan tentang Mel Frequency Cepstrum Coefficients (MFCC). Berdasarkan hasil kajian dapat dibuat kerangka kerja aplikasi yang dibuat. Kerangka kerja yang disusun merupakan kerangka kerja yang umum dilakukan pada aplikasi dan penelitian tentang penggunaan data suara dan bunyi. Selain itu kajian ini akan menjabarkan tentang lingkup penelitian bunyi dan suara yang telah banyak dilakukan. Melalui penjabaran tentang lingkup penelitian didapatkan peluang penelitian yang dapat dilakukan pada data bunyi dan suara serta tantangannya.

E-commerce Design Interaction with Voice User Interface using User-centered Design Approach Rizky Faramita; Dessi Puji Lestari; Ginar Santika Niwanputri
IJNMT (International Journal of New Media Technology) Vol 6 No 2 (2019): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

The rapid expansion of e-commerce has encouraged many platforms to serve their consumers better, including by providing state-of-the-art user interaction. Voice user interface is integrated in the e-commerce in order to allow users doing multitask while having handful activities and simplify features whose discoverability is low. The interface is designed using user-centered design approach, specifically ISO 9241-210:2010 methodology. In addition, the interface is verified by usability testing conducted in three iterations for two personas. Verification process of the design shows that high-fidelity prototype is 83.0% helpful and 70.0% effective.

Designing Mobile Application Interaction for School Internal Communication using User-centered Design Alivia Dewi Parahita; Dessi Puji Lestari; Ginar Santika Niwanputri
IJNMT (International Journal of New Media Technology) Vol 7 No 1 (2020): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

In order to fastened up the process, the school internal communication system has been changed from its conventional way using linking book and announcement letter, to messaging groups or mobile applications. This was done to create a good communication between teachers and parents for their children. But parent capability on using mobile application has been causing problems. In this paper, we present the result of our observation for the current school internal communication system problem and made the better solution. To make sure the application design matches with parent’s capability, the user-centered design method is applied. The final result of this study is a high-fidelity prototype of the application for parent side built using Sketch tools. The usability testing has been done to student parents. Based on the test result, this prototype has a good interaction, has a user-friendly interface for parents, and also already fulfill the usability goal and the user experience.

XGBoost and Convolutional Neural Network Classification Models on Pronunciation of Hijaiyah Letters According to Sanad Aaz Muhammad Hafidz Azis; Dessi Puji Lestari
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1081

According to Sanad, the pronunciation of Hijaiyah letters can serve as a benchmark for correct or valid reading based on the makhraj and properties of the letters. However, the limited number of Qur'anic Sanad teachers remains one of the obstacles to learning the Qur'an. This study aims to identify the most practical combination of classification models in constructing a voice recognition system that facilitates learning without requiring direct interaction with a teacher. The methods employed include the XGBoost algorithm and CNN. As a result, out of the 12 letter trait labels, the CNN model was utilized for 10 of them, specifically for traits S1, S2, S4, S5, T1, T2, T3, T4, T5, and T6, on trait labels S3 and T7 applying the XGBoost model. Furthermore, the inclusion of additional data yielded performance results for each property, with an average accuracy of 78.14% for property S (letters with opposing properties), 70.69% for property T (letters without opposing properties), and an overall average of 73.79% per letter.

GAN-Based End to End Text-to-Speech System for Indonesian Language Dhiaulhaq, Moch Azhar; Ginanjar, Rizki Rivai; Lestari, Dessi Puji
Jurnal Linguistik Komputasional Vol 5 No 2 (2022): Vol. 5, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v5i2.115

The developments of the modern text-to-speech (TTS) technology have matured in which the direction of the recent approaches has moved toward the optimization of the system and TTS modeling from the resource-scarce languages, rather than finding new model architectures. In this paper, a novel approach to modeling modern end-to-end (E2E) TTS for Indonesian language with the integration of three different generative adversarial networks (GAN)-based vocoders for comparison is proposed. Based on the evaluation, the proposed system shows promising results with the mean opinion score (MOS) value of 4.60 while still maintaining fast inference speed, proven by the real-time factor (RTF) value under one.

XGBoost and Convolutional Neural Network Classification Models on Pronunciation of Hijaiyah Letters According to Sanad Azis, Aaz Muhammad Hafidz; Lestari, Dessi Puji
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1081

According to Sanad, the pronunciation of Hijaiyah letters can serve as a benchmark for correct or valid reading based on the makhraj and properties of the letters. However, the limited number of Qur'anic Sanad teachers remains one of the obstacles to learning the Qur'an. This study aims to identify the most practical combination of classification models in constructing a voice recognition system that facilitates learning without requiring direct interaction with a teacher. The methods employed include the XGBoost algorithm and CNN. As a result, out of the 12 letter trait labels, the CNN model was utilized for 10 of them, specifically for traits S1, S2, S4, S5, T1, T2, T3, T4, T5, and T6, on trait labels S3 and T7 applying the XGBoost model. Furthermore, the inclusion of additional data yielded performance results for each property, with an average accuracy of 78.14% for property S (letters with opposing properties), 70.69% for property T (letters without opposing properties), and an overall average of 73.79% per letter.

Comparing Pre-Norm and Post-Norm Transformers in Preserving Gender Information for Indonesian–English Translation through Attention-Based Signal Reinforcement Andik Wijanarko; Rinaldi Munir; Masayu Leylia Khodra; Dessi Puji Lestari
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1257

Gender realization in Indonesian–English machine translation remains challenging due to the absence of grammatical gender in Indonesian, which often leads to unstable or ambiguous gender representations in English outputs. While Transformer-based models have demonstrated strong general translation performance, their ability to preserve gender information across encoding layers remains inconsistent and poorly understood, particularly with respect to architectural normalization strategies.This study presents a comparative analysis of Pre-Norm and Post-Norm Transformer architectures in preserving gender information, and examines the role of attention-based signal reinforcement in mitigating representational degradation. The reinforcement mechanism is introduced prior to standard encoder processing to strengthen gender-relevant token interactions without modifying the overall model structure.Four controlled configurations—Post-Norm, Pre-Norm, Post-Norm with attention-based reinforcement, and Pre-Norm with attention-based reinforcement—are trained under identical random seeds on both unbalanced and balanced datasets. Evaluation is performed on gender-ambiguous test sentences without explicit gender annotations to assess generalization. Gender preservation is assessed at the output level using gender-specific accuracy and BLEU score, and at the representation level using cosine similarity between gender cue embeddings and English gendered pronouns.The results show that Post-Norm Transformers fail to maintain stable gender representations, yielding near-random gender accuracy (~50%) and negligible BLEU scores. Pre-Norm architectures improve training stability but achieve limited gender accuracy (around 30%). Incorporating attention-based signal reinforcement substantially enhances gender preservation, with accuracy rising to over 50% and reaching up to 56% under balanced training conditions, accompanied by a consistent increase in cosine similarity values (exceeding 0.35) between gender cues and corresponding pronouns. These findings indicate that normalization strategy and attention-based reinforcement jointly determine the stability of gender representations in Transformer-based machine translation.

Minimal Gated Recurrent Unit with Temporal Convolution Based Acoustic Modeling on Speech Recognition System for Evaluating the Recitation of Quran Isjhar Kautsar; Dessi Puji Lestari; Aulia Rahmawati
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1613

The use of future context in acoustic modeling seems to give an impact on system performance such us Bidirectional Long Short-Term Memory (BLSTM). It has been used as an acoustic model on Speech Recognition System for Quran recitation and show better result than Hidden Markov Model - Gaussian mixture model (HMM-GMM) with average Word Error Rate (WER) value 4.6%. but, the architectural complexity of BLSTM make the latency during decoding process is high. To reduce the latency, Minimal Gated Recurrent Unit with Temporal Convolution (mGRUIPTC) acoustic model was used. Text data such as transcription, lexicon, and corpus used in training are represented at phone level to handle phone level detection. The transcription is generated using modified QScript to handle reciting rules in detail. In the test, the system can reduce decode process latency by up to 11 seconds with Phone Error Rate (PER) difference of up to 1.46% compared to BLSTM. However, our model still needs to be trained with more data to detect error recitation better.

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