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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknologi dan Rekayasa TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah KOMPUTASI Jurnal Teknik Elektro dan Komputer TRIAC Jurnal E-Komtek Indonesian Journal of Electrical Engineering and Computer Science
Ajib Setyo Arifin
Universitas Indonesia
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Music genre classification using Inception-ResNet architecture Valdera, Fauzan; Arifin, Ajib Setyo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3300-3310

Abstract

Music genres help categorize music but lack strict boundaries, emerging from interactions among public, marketing, history, and culture. With Spotify hosting over 80 million tracks, organizing digital music is challenging due to the sheer volume and diversity. Automating music genre classification aids in managing this vast array and attracting customers. Recently, convolutional neural networks (CNNs) have been used for their ability to extract hierarchical features from images, applicable to music through spectrograms. This study introduces the Inception-ResNet architecture for music genre classification, significantly improving performance with 94.10% accuracy, precision of 94.19%, recall of 94.10%, F1-score of 94.08%, and 149,418 parameters on the GTZAN dataset, showcasing its potential in efficiently managing and categorizing large music databases.
Analisis Penerimaan Pengguna 5G Di Jabodetabek Dengan Metode Kombinasi UTAUT-TTF Lukianto, Chandra Septian; Arifin, Ajib Setyo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Penerapan jaringan 5G di Indonesia, khususnya di wilayah Jabodetabek (Jakarta, Bogor, Depok, Tangerang, dan Bekasi), merupakan langkah penting dalam mengakomodasi kebutuhan akan layanan internet yang lebih cepat dan lebih andal. Namun, adopsi teknologi ini tidak hanya bergantung pada kemampuan teknologinya, tetapi juga pada faktor-faktor yang mempengaruhi persepsi dan penerimaan pengguna. Dengan demikian, penelitian ini bertujuan untuk menganalisis perilaku penggunaan jaringan 5G dengan mengidentifikasi faktor-faktor yang mempengaruhi masyarakat dalam menggunakan jaringan 5G berdasarkan metode UTAUT-TTF. Hasil survei melibatkan 113 responden serta analisis data dilakukan menggunakan metode SEM-PLS pada pengguna jaringan 5G di Jabodetabek. Hasil penelitian ini menunjukkan bahwa berbagai faktor dalam model integrasi UTAUT-TTF memainkan peran penting dalam menentukan adopsi teknologi 5G di wilayah Jabodetabek. Faktor-faktor seperti kondisi yang memfasilitasi, kinerja yang diharapkan, kesesuaian antara tugas teknologi, dan karakteristik teknologi memiliki pengaruh yang signifikan dan positif terhadap adopsi pengguna, sementara kemudahan penggunaan dan pengaruh sosial menunjukkan pengaruh yang lebih kompleks atau tidak signifikan. Hal ini menyoroti pentingnya tidak hanya fokus pada kemudahan penggunaan teknologi, tetapi juga memperhatikan faktor-faktor eksternal dan kecocokan teknologi dengan tugas-tugas spesifik yang dilakukan oleh pengguna. Dengan memahami dinamika ini, penyedia layanan serta pengembang kebijakan dan teknologi dapat lebih efektif dalam merancang strategi adopsi yang sesuai dengan kebutuhan dan preferensi pengguna, serta memastikan bahwa infrastruktur yang mendukung tersedia dan memadai untuk mendukung penggunaan teknologi 5G secara optimal. Abstract The deployment of 5G networks in Indonesia, especially in the Jabodetabek area (Jakarta, Bogor, Depok, Tangerang, and Bekasi), is a significant step in meeting the demand for faster and more reliable internet access. However, its adoption depends not only on technological capabilities but also on user perceptions and acceptance. This research explores factors influencing public adoption of 5G networks using the UTAUT-TTF method. A survey of 113 respondents in Jabodetabek was analyzed using the SEM-PLS method. The results of this study indicate that various factors in the UTAUT-TTF integration model play an important role in determining the adoption of 5G technology in the Jabodetabek area. Factors such as facilitating conditions,  performance expectancy, task technology fit, and technology characteristics have a significant and positive influence on user adoption, while ease of use and social influence show a more complex or insignificant influence. This highlights the importance of not only focusing on the ease of use of technology, but also paying attention to external factors and the suitability of technology to specific tasks performed by users. By understanding these dynamics, service providers and policy and technology developers can be more effective in designing adoption strategies that are in accordance with user needs and preferences, and ensuring that supporting infrastructure is available and adequate to support optimal use of 5G technology.
OPTIMALISASI JUMLAH RING DAN KLUSTER PADA SKEMA ROUTING LORAWAN BERBASIS VARIASI MODEL SEBARAN SENSOR Raden Andhika Ajiesastra; Ajib Setyo Arifin; Dadang Gunawan
Jurnal Teknik Elektro dan Komputer TRIAC Vol 10, No 1 (2023): Mei 2023
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v10i1.19729

Abstract

Teknologi Long Range Wide Area Network (LoRaWAN) yang sangat pesat untuk sistem IoT tidak dapat disangkal karena komunikasi nirkabel LPWAN memimpin dalam skalabilitas, masa pakai, kapasitas, biaya, dan jarak transmisi. terutama karakteristik LoRaWAN yang dapat melakukan transmisi jarak jauh dan konsumsi energi yang rendah. Namun, konfigurasi jaringan yang tepat penting dilakukan untuk proses pertukaran paket data agar semua node dapat terhubung dengan konsumsi energi rendah. Menemukan konfigurasi yang optimal untuk mendapatkan hasil maksimal dari protokol transmisi LoRa merupakan hal yang tidak terpisahkan sebelum penerapan. Distance-Ring Exponential Stations Generator (DRESG) menghadirkan skema perutean yang memungkinkan perutean multi-hop dan mengurangi konsumsi energi node. Penelitian ini menggunakan simulasi model matematis untuk memperbaiki metode tersebut dengan mengimplementasikan pembagian klaster pada busur lingkaran dengan berbagai skema penyebaran node, yang memungkinkan sistem memiliki mekanisme perutean dinamis yang menyesuaikan dengan distribusi node. Hasil simulasi menunjukkan dengan kombinasi skema distribusi node random uniform centered dapat meningkatkan efisiensi energi antara jumlah virtual ring dan node adalah  3 ring dan 6 ring,  nilai bebas atau merata dari div 2 sampai dengan div 8 dengan critical node pada ring 1 dan memiliki energi terendah sebesar 0,56 mJ.
Fast and Accurate Voice Biometrics with Deep Learning Algorithm of CNN Depthwise Separable Convolution Model and Fusion of DWT-MFCC Methods Haris Isyanto; Ajib Setyo Arifin; Muhammad Suryanegara
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.24515

Abstract

Theft of private data became a threat of crime in cyberspace. This issue was in line with rapid development of data technology, especially online transactions. To attenuate this problem, voice biometrics was developed as an answer to keep up security identity. This paper develops the voice biometric framework based on Convolutional Neural Network Depthwise Separable Convolution (DSC) model and the fusion of Discrete Wavelet Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC). Such a scheme has targeted to increase the high accuracy, to reduce the burden of high computational costs and to speed up the performance of classification process time. We conduct three testing performance, i.e. voice Biometric Training Performance, speaker Recognition Performance (”Who is speaking?”), and Speech Recognition performance (”What keyword is uttered?”).  For each of the testing, the results are compared with CNN Standard performance. The training results has shown that CNN DSC model has reduced the amount of training parameters to 364,506, leading to accelerate the performance of training process time to 5.12 minutes. The results of speaker recognition performance has attained the best performance with an accuracy 99.25%, precision 97.14%, recall 98.17% and F1-score 97.28%. The results of speech recognition performance has been able to improve the best performance with accuracy 100%. It can be concluded that CNN DSC with the fusion of DWT- MFCC has outperformed the CNN Standard. The framework can be applied for the identification and verification of user voices accurately, quickly and efficiently for any applications requiring better security performance.
The prediction of mobile data traffic based on the ARIMA model and disruptive formula in industry 4.0: A case study in Jakarta, Indonesia Ajib Setyo Arifin; Muhammad Idham Habibie
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.12989

Abstract

Disruptive technologies, which are caused by the cellular evolution including the Internet of Things (IoT), have significantly contributed data traffic to the mobile telecommunication network in the era of Industry 4.0. These technologies cause erroneous predictions prompting mobile operators to upgrade their network, which leads to revenue loss. Besides, the inaccuracy of network prediction also creates a bottleneck problem that affects the performance of the telecommunication network, especially on the mobile backhaul. We propose a new technique to predict more accurate data traffic. This research used a univariate Autoregressive Integrated Moving Average (ARIMA) model combined with a new disruptive formula. Another model, called a disruptive formula, uses a judgmental approach based on four variables: Political, Economic, Social, Technological (PEST), cost, time to market, and market share. The disruptive formula amplifies the ARIMA calculation as a new combination formula from the judgmental and statistical approach. The results show that the disruptive formula combined with the ARIMA model has a low error in mobile data forecasting compared to the conventional ARIMA. The conventional ARIMA shows the average mobile data traffic to be 49.19 Mb/s and 156.93 Mb/s for the 3G and 4G, respectively; whereas the ARIMA with disruptive formula shows more optimized traffic, reaching 56.72 Mb/s and 199.73 Mb/s. The higher values in the ARIMA with disruptive formula are closest to the prediction of the mobile data forecast. This result suggests that the combination of statistical and computational approach provide more accurate prediction method for the mobile backhaul networks.
Co-Authors Adli, Fikri Faisal Ajiesastra, Raden Andhika Akhmad Sarif Arwidya Tantri Agtusia A’isya Nur Aulia Yusuf Dadang Gunawan Dina Kusuma Wahyuni Fery Andriyanto Fitri Yuli Zulkifli Haris Isyanto I Wayan Mustika Lukianto, Chandra Septian M. B. Fathinah Hanun Maulana, Eka Muhammad Asvial Muhammad Daniel Muhammad Idham Habibie Muhammad Suryanegara Raden Andhika Ajiesastra Tomoaki Ohtsuki Valdera, Fauzan