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All Journal Teknotan: Jurnal Industri Teknologi Pertanian Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer International Journal of Artificial Intelligence Research Jurnal Elektronika dan Telekomunikasi Jurnal Teknologi Informasi dan Multimedia Jurnal Abdi Insani Jurnal Bumigora Information Technology (BITe) Journal of Applied Data Sciences Journal of Community Development Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bakti Sekawan : Jurnal Pengabdian Masyarakat Paradharma: Jurnal Aplikasi IPTEK Jurnal INFOTEL International Journal Research on Metaverse
Syahroni Hidayat
Universitas Negeri Semarang
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Sistem Pengenalan Pembicara dengan Metode Wavelet-MCFF dan Pengklasifikasi Hidden Markov Models (HMM) Hidayat, Syahroni; Anas, Andi Sofyan; Yusuf, Siti Agrippina Alodia; Tajuddin, Muhammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0813284

Abstract

Penelitian pengolahan sinyal digital yang berfokus pada pengenalan pembicara telah dimulai sejak beberapa dekade yang lalu, dan telah menghasilkan banyak metode-metode pengenalan pembicara. Di antara algoritma pembentukan koefisien ciri yang telah dikembangkan tersebut, ada dua algoritma yang dapat memberikan akurasi yang tinggi jika diterapkan pada sistem, yaitu Mel Frequency Cepstral Coefficient (MFCC) dan Wavelet. Penelitian ini bertujuan untuk menguji dan memilih kanal terbaik dari proses wavelet-MFCC yang dapat dijadikan sebagai koefisien ciri baru untuk diterapkan pada sistem pengenal pembicara. Koefisien ciri baru tersebut kemudian disebut dengan koefisien ciri Wavelet-MFCC. Kofisien ini dibentuk dari merubah kanal hasil dekomposisi wavelet, yaitu kanal aproksimasi (cA), kanal detail (cD), dan penggabungannya (cAcD), menjadi koefisien MFCC. Metode dekomposisi wavelet yang digunakan adalah metode dyadic dengan menerapkan level dekomposisi level 1 dan level 2. Setiap koefisien ciri kemudian menjadi inputan pada sistem pengklasifikasi Hidden Markov Models (HMM). Keluaran dari HMM kemudian dihitung akurasinya dan dianalisis. Dari pengujian yang dilakukan, diperoleh bahwa kanal detail (cD) sebagai ciri dapat memberikan akurasi yang sama dengan menggunakan kanal gabungan (cAcD) dan lebih tinggi dari kanal aproksimasi (cA), dengan akurasi sebesar 95%. Hal ini menunjukkan bahwa, kanal detail pada dekomposisi level 1 menyimpan ciri suara dari setiap pembicara sehingga sudah cukup untuk dijadikan sebagai koefisien ciri. Maka, penggunaan dekomposisi level 1 dan kanal detail cD sebagai ciri Wavelet-MFCC pada sistem pengenalan pembicara dapat meringankan dan mempercepat proses komputasi. AbstractResearch in digital signal that focused on speaker recognition has begun since decades ago, and has resulted many speaker recognition methods. there are two algorithms that can provide high accuracy in recognition system, which are Mel Frequency Cepstral Coefficient (MFCC) and Wavelet. the aims of this study is to examine and chose the best channel from wavelet-MFCC process that can be used as new feature coefficient, then called as Wavelet-MFCC features coefficient. The coefficient is built by converting the wavelet decomposition channels, which are approximation (cA), detail (cD), and its combination (cAcD), into the MFCC coefficient. Wavelet dyadic decomposition with level 1 and level 2 of decomposition is applied. Each feature coefficient acts as an input to the HMM classifier. The accuracy of the HMM output is calculated, then analyzed. The obtained results show that the detail chanel (cD) achieve equal accuracy as the combination chanel (cAcD), and higher accuracy compared to aproximation channel (cA), with accuracy 95%. Thus, it can be conclude that the detail channel on level 1 decomposition contains features of each speaker's. Then, cD is enough to be used as a Wavelet-MFCC feature. Thus, its implementation in the SRS can ease and speed up the computing process.
Transformasi Lontar Babad Lombok Menuju Digitalisasi Berbasis Natural Gradient Flexible (NGF) Anwar, Muhammad Tajuddin; Hidayat, Syahroni; Adil, Ahmat
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 2: April 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021824088

Abstract

Suku Sasak, yang tinggal di pulau Lombok Nusa Tenggara Barat, memiliki tradisi penulisan di daun lontar (Borassus Flabellifer) kering, salah satunya adalah naskah Lontar Babad Lombok. Naskah Lontar Babad Lombok seiring berlalunya waktu, menjadi rapuh dan mudah patah sehingga memerlukan perawatan. Keadaan ini mendorongnya perlu dilakukan digitalisasi naskah lontar babad lombok sebagai bentuk pelestarian sehingga para generasi Milenial, khususnya di Lombok, dapat menikmati lontar babad lombok. Digitalisasi citra tersebut tantangan utama adalah tepi kabur teks dan perbedaan minimum antara teks dan bagian non-tekssebagai akibat dari proses perawatan. Oleh karena itu, dibutuhkan proses peningkatan kualitas citra hasil digitalisasi agar tulisan dapat lebih jelas terbaca. Salah satu metode yang terbukti mampu untuk memisahkan teks dari latar belakang yang sangat berkorelasi adalah Natural Gradient Flexibel (NGF) berbasiskan Independent Component Analysis (ICA), NGF-ICA. Penelitian ini bertujuan untuk melakukan peningkatan kualitas citra digitalisasi sebelum diumpankan pada database dan sistem informasi yang telah dibangun. Kualitas citra yang telah ditingkatkan diukur menggunakan metode MSE dan PSNR untuk tingkat kemiripannya, dan metode Entropi dan SSIM untuk informasi dan perspektif visual. Hasil penelitian menunjukkan bahwa penerapan algoritma NGF-ICA dapat memberikan citra keluaran dengan kualitas yang tinggi dengan nilai rata-rata MSE, PSNR, SSIM dan peningkatan Entropi sebesar 708, 19.95 db, 0.87 dan 0.45, secara berturut-turut. AbstractSasak tribe, who lives on Lombok Island, West Nusa Tenggara, has been writing manuscripts on dry palm leaves (Borassus Flabellifer) as a tradition, one of the manuscripts is Lontar Babad Lombok. As time pass by, the manuscript becomes brittle and breaks easily, therefore maintenances are required. this situation force the need to digitalize the manuscript as an act of preservation, hence the millennial generation, especially on Lombok Island, can enjoy the manuscript. the main challenge is the blurry edge of the text and the slight difference between the text and non-text part caused by the treatment process. Hence, it is needed to enhance the quality of the digitalize image to make the manuscript can be more clearly read. One of the proven methods that able to separate text from highly correlated backgrounds is Natural Gradient Flexibel (NGF) based on Independent Component Analysis (ICA), NGF-ICA. The aim of this study is to improve the quality of the digitized images before they fed into the database and information system that has been built. The enhanced image quality was measured, MSE and PSNR methods were used to measure the similarity level, and the Entropy and SSIM method were used to measure the information and visual perspective. The results show that the application of the NGF-ICA algorithm can generate high-quality output images with average values of MSE, PSNR, SSIM, and increasing Entropy by 708, 19.95 dB, 0.87, and 0.45, respectively.
Pelatihan Konten E-Commerce bagi Kelompok Kerajinan Bambu Dusun Dasan Bare untuk Menyongsong Era Digital Market Budiarto, Jian; Hidayat, Syahroni; Rizal, Ahmad Ashril
Paradharma: Jurnal Aplikasi IPTEK Vol. 3 No. 2 (2019): Paradharma: Jurnal Aplikasi IPTEK
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Dhyana Pura – Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.076 KB) | DOI: 10.36002/jpd.v3i2.1045

Abstract

Tujuan dilaksanakannya Program Kemitraan Masyarakat (PKM) Kelompok Kerajinan Bambu Dasan Bare adalah untuk meningkatkan kegiatan ekonomi di tempat tersebut. Semakin meningkatnya jumlah pengguna teknologi internet sebagai media informasi saat ini dapat menjadi peluang bagi penjual agar berbondong-bondong untuk melakukan penjualan secara online. Pada umumnya pengusaha UKM sudah mulai menerapkan e-commerce karena banyaknya manfaat yang diperoleh oleh pengusaha dari e-commerce. Meskipun demikian penerapan e-commerce juga memiliki hambatan. Faktor-faktor penghambat tersebut di antaranya sumber daya yang kurang mampu untuk bersaing dalam dunia teknologi, kurangnya informasi mengenai e-commerce, kebiasaan masyarakat Indonesia yang melihat dan merasakan secara langsung apa yang dibeli, keamanan transaksi dan lainnya. Oleh karena itu, sangat perlu dilakukan pelatihan penggunaan e-commerce bagi pengusaha UKM kerajinan agar dapat melakukan promosi penjualan yang lebih efektif dan efisien. Sehingga, permasalahan seperti peningkatan pengetahuan mengenai teknologi informasi oleh UKM, aplikasi website yang kurang menarik dan penyiapan konten (teks dan gambar) dapat diatasi. Metode pelaksanaan dari kegiatan PKM dilaksanakan melalui tahap persiapan, pelaksanaan pelatihan, penyajian materi, penugasan praktik, evaluasi dan editing konten e-commerce hingga refleksi dan penutupan program.Kata kunci: e-commerce, kerajinan bambu-rotan, LombokABSTRACTThe aim of Public Partnership Program for bamboo craftsman in Dasan Bare is to improve economic activity. Nowadays, online transactions to selling their product is increasing. Seller has been implement e-commerce to promote their product. Some factor to restrict implementation of e-commerce is the people don’t have enough information how to use e-commerce. So that, we propose to give training how to prepare content and using e-commerce application. This training using some effective methods such as preparing tools, preparing content, training application, practical task, evaluation and editing ecommerce virtual shop.Keywords: e-commerce, bamboo-rattan crafts, Lombok
Ensemble Implementation for Predicting Student Graduation with Classification Algorithm Rismayati, Ria; Ismarmiaty, Ismarmiaty; Hidayat, Syahroni
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1805

Abstract

Graduating on time at the higher education level is one of the main targets of every student and university institution. Many factors can affect a student's length of study, the different character of each student is also an internal factor that affects their study period. These characters are used in this study to classify data groups of students who graduated on time or not. Classification was chosen because it is able to find a model or pattern that can describe and distinguish classes in a dataset. This research method uses the esemble learning method which aims to see student graduation predictions using a dataset from Kaggle, the data used is a IPK dataset collected from a university in Indonesia which consists of 1687 records and 5 attributes where this dataset is not balanced. The intended target is whether the student is predicted to graduate on time or not. The method proposed in this study is Ensemble Learning Different Contribution Sampling (DCS) and the algorithms used include Logistic Regression, Decision Tree Classifier, Gaussian, Random Forest Classifier, Ada Bost Classifier, Support Vector Coefficient, KNeighbors Classifier and MLP Classifier. From each classification algorithm used, the test value and accuracy are calculated which are then compared between the algorithms. Based on the results of research that has been carried out, it is concluded that the best accuracy results are owned by the MLPClassifier algorithm with the ability to predict student graduation on time of 91.87%. The classification model provided by the DCS-LCA used does not give better results than the basic classifier of its constituent, namely the MLPClassifier algorithm of 91.87%, SVC of 91.64%, Logistic Regression of 91.46%, AdaBost Classifier of 90.87%, Random Forest Classifier of 90.45% , and KNN of 89.80%.
Classification of Game Genres Based on Interaction Patterns and Popularity in the Virtual World of Roblox Hasanah, Uswatun; Sunarko, Budi; Hidayat, Syahroni; Rachmawati, Rina
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.30

Abstract

The rapid growth of user-generated virtual environments has elevated the importance of understanding player behavior and content dynamics in metaverse platforms. This study investigates the relationship between game genres and user engagement in Roblox, one of the largest and most interactive virtual worlds. Utilizing a dataset of over 300 game entries, we analyzed engagement metrics including visits (ranging from thousands to over 2.8 billion), likes (up to 1,000,000), favorites (up to 3.4 million), and active user counts (as high as 22,155). Descriptive statistics and correlation analysis revealed that action-oriented genres—particularly Action, Shopping, and Obby & Platformer—consistently outperform others in attracting and retaining users. The strong positive correlation between likes and favorites (r = 0.95) indicates that user satisfaction strongly predicts long-term interest, while negative feedback (dislikes) shows minimal correlation with other variables. In contrast, genres such as Education and Entertainment demonstrated significantly lower averages, with visits below 1 million, and active user counts typically under 1,000. These findings provide practical insights for developers and platform administrators seeking to optimize content strategies and offer a foundation for future research involving clustering analysis, sentiment mining, and temporal behavior modeling to enhance recommendation systems and genre personalization within metaverse ecosystems.
Optimalisasi Model Ensemble Learning dengan Augmentasi dan SMOTE pada Sistem Pendeteksi Kualitas Buah Syahroni Hidayat; Taofan Ali Achmadi; Hanif Ardhiansyah; Hanif Hidayat; Rian Febriyanto; Abdulloh Abdulloh; Intan Ermawati
Jurnal Teknologi Informasi dan Multimedia Vol. 6 No. 1 (2024): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i1.406

Abstract

Fruit quality is an important factor in selecting fruit for consumption because it affects consumer health and satisfaction. Identification of fruit quality has become the focus of research, and one of the approaches used is a non-destructive approach through measuring the gases produced by the fruit. Machine learning can be used to process this gas data and build system models that can classify fruit quality. This research discusses the application of the DCS-OLA and Stacking dynamic ensemble learning algorithms to build a fruit quality detection system model. The basic methods used to build models are Logistic Regression, Decision Tree, Gaussian Naïve Bayes, and Mul-ti-Layer Perceptron. The fruit used is mango with a shelf life of 7 days and Srikaya (sugar apple) with a shelf life of 4 days. The condition of the initial dataset is unbalanced. The research results show that trimming the mango dataset to only 4 days according to the shelf life of sugar apple helps reduce the difference in shelf life between the two. Then jittering and balancing techniques are used to increase and balance the number of datasets between the two types of fruit. High accuracy is achieved by the DCS-OLA ensemble and stacking ensemble by combining the basic methods of Logistic Regression and Decision Tree, especially in balanced dataset conditions. In conclusion, the use of ensemble learning in detecting fruit quality has great potential for real-world applications. However, further validation is needed with larger datasets and a wider variety of conditions.
Acoustic Analysis on Cleft Lip Speech Signal Sitti Agripina Alodia Yusuf; Nani Sulistianingsih; Muhammad Imam Dinata; Syahroni Hidayat; Joelianto Darmawan
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.766

Abstract

Cleft conditions significantly disrupt phonetic articulation, leading to hypernasality and irregular resonance characteristics. In this study, the formant analysis of normal and cleft speech is presented, with the aim of investigating acoustic differences in formant frequencies between cleft and normal speech using real-word utterances, focusing on the articulation of plosive consonants and resonance variability.  The dataset consisted of 280 speech signals (140 cleft and 140 normal) uttering word /paku/. The speech signals were resampled to 16kHz and the silence in the speech was removed, next stage was followed by extracting the first three formants using the Burg algorithm. Statistical analysis revealed that the value of F1 and F2 in cleft speech were higher, alongside greater variability in formant distribution. Further analysis of plosive articulation highlighted irregular formant transition in cleft speech, indicating compromised intraoral pressure control. Additionally, a moderate negative correlation (r = -0.423, p<0.001) between F1 and F3 suggests a spectral pattern indicative of hypernasality. This finding underscores the potential of formant-based acoustic features as objective markers for early clinical assessment and provides a foundation for the development of diagnostic models in cleft speech research.
Wavelet-Based MFCC and CNN Framework for Automatic Detection of Cleft Speech Disorders Muhammad Hilmy Herdiansyah; Syahroni Hidayat; Nur Iksan
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.780

Abstract

Cleft Lip and Palate (CLP) is a congenital condition that often results in atypical speech articulation, making automatic recognition of CLP speech a challenging task. This study proposes a deep learning-based classification system using Convolutional Neural Networks (CNN) and Wavelet-MFCC features to distinguish speech patterns produced by CLP individuals. Specifically, we investigate the use of two wavelet families Reverse Biorthogonal (rbio1.1) and Biorthogonal (bior1.1)—with three decomposition strategies: single-level (L1), two-level (L2), and a combined level (L1+2). Speech data were collected from 10 CLP patients, each pronouncing nine selected Indonesian words ten times, resulting in 900 utterances. The audio signals were processed using wavelet-based decomposition followed by Mel-Frequency Cepstral Coefficients (MFCC) extraction to generate time-frequency representations of speech. The resulting features were input into a CNN model and evaluated using 5-fold cross-validation. Experimental results show that the combined L1+2 decomposition yields the highest classification accuracy (92.73%), sensitivity (92.97%), and specificity (99.04%). Additionally, certain words such as “selam”, “kapak”, “baju”, “muka”, and “abu” consistently achieved recall scores above 0.94, while “lampu” and “lembab” proved more difficult to classify. The findings demonstrate that integrating multi-level wavelet decomposition with CNN significantly improves the recognition of pathological speech and offers promising potential for clinical diagnostic support.
Co-Authors Abdulloh Abdulloh Abdurahim, Abdurahim Adam Bachtiar Maulachela Agung Budiwirawan Agus Ardiyanto Ahmad Zuli Amrullah Ahmat Adil Akbar Juliansyah Anan Nugroho Ananda, Briska Putra Andi Sofyan Anas Ansar Ansar Ardiansyah, Muhammad Irfan Baroroh, Luluk Taufiqul Budi Sunarko Budiarto, Jian Danang Tejo Kumoro Danang Tejo Kumoro Dian Syafitri Chani Saputri Diyanasari, Ledi Esa Apriaskar Feddy Setio Pribadi Fikri, Akmal Habib Ratu Perwira Negara Haikal Abror Hakiki, Muhammad Khikam Hanif Ardhiansyah Hanif Hidayat Intan Ermawati Ismarmiaty Ismarmiaty, Ismarmiaty Joelianto Darmawan Joko Sumarsono Khoirudin Fathoni, Khoirudin Kumoro, Danang Tejo Mona Subagja Muhammad Fathurrahman Muhammad Hilmy Herdiansyah Muhammad Imam Dinata Muhammad Muhammad MUHAMMAD TAJUDDIN Muhammad, Naufal Murad Murad Murad, Murad Nani Sulistianingsih Ni Luh Putu Merawati Nugroho, Anan Nur Iksan Qudsi, Jihadil R Fanny Priniti Raden Fanny Printi Ardi Rahmat Sabani Rezky Ramdhaningsih Ria Rismayati Rian Febriyanto Rina Rachmawati Risanuri Hidayat Rismayati, Ria Rizal, Ahmad Ashril Salim, Nur Azis Sandi Justitia Putra Satria, Rifki Lukman Simanjuntak, Jhonatur Stheven Sitti Agripina Alodia Yusuf Subagja, Mona Sukmawaty Sukmawaty Sukmawaty Sukmawaty Sulistyawan, Vera Noviana Tajuddin, Muhammad Taofan Ali Achmadi Teguh Bharata Adji Uswatun Hasanah USWATUN HASANAH Uswatun Hasanah Wafi, Ahmad Zein Al Wahyudi, Tri Agus Yusuf, Siti Agrippina Alodia Zaenal Abidin Zaurarista Dyarbirru Zidan Vieri Wijaya