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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknoinfo Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknik Informatika (JUTIF) JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Teknologi dan Sistem Tertanam Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Telematics and Information Technology (TELEFORTECH) Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Paradigma Journal of Engineering and Information Technology for Community Service Bulletin of Informatics and Data Science Jurnal Ilmiah Computer Science CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Data Science and Information System Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Jurnal Media Jawadwipa
Priandika, Adhie Thyo
Universitas Teknokrat Indonesia
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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Program Sekolah Binaan : In House Training Peningkatan Kompetensi Public Speaking Dalam Kepemimpinan Siswa Di SMAN 2 Gedong Tataan Sulistiyawati, Ari; Yulianti, Tien; Rahmanto, Yuri; Fitratullah, M.; Priandika, Adhie Thyo
Journal of Social Sciences and Technology for Community Service (JSSTCS) Vol 4, No 2 (2023): Volume 4, Nomor 2, September 2023
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v4i2.3199

Abstract

Kegiatan Pengabdian kepada Masyarakat ini dilakukan pada mitra sekolah binaan di SMA Negeri 2 Gedong Tataan. Program yang dilakukan adalah In house training peningkatan kompetensi public speaking yang melibatkan semua pengurus OSIS dan perwakilan siswa kelas X dan XI sesuai program kerja yang disetujui oleh pihak sekolah.  Permasalahan yang dialami oleh mitra yaitu: belum optimalnya kemampuan public speaking untuk menunjang kepemimpinan yang berkualitas dalam organisasi di sekolah. Solusi yang diusulkan untuk mengatasi permasalahan tersebut adalah peningkatan softskill bagi siswa terpilih untuk mengikuti bimbingan dan pelatihan public speaking dalam keterampilan berbicara. Target luaran dari kegiatan PKM Sekolah Binaan ini adalah 1) peningkatan kemampuan siswa yang diukur melalui kuesioner, 2) artikel publikasi di jurnal ABDIMAS terakreditasi nasional, 3) artikel berita kegiatan yang dishare di media massa online,  dan 4) video kegiatan di link youtube LPPM Teknokrat
Combination of Logarithmic Least Square Weighting and MAUT Method for Best Employee Selection in Retail Companies Saputra, Aditya; Priandika, Adhie Thyo
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/mf9wad40

Abstract

Selecting the best employees plays a crucial role in enhancing the performance of retail companies. Given that each employee has unique roles, responsibilities, and working conditions, creating a truly fair and consistent assessment standard can be challenging. Additionally, subjective factors such as personal bias or preferences of the assessor can influence the evaluation outcome. The integration of LLSW and the MAUT method in employee selection offers a systematic approach that combines precise weighting with multi-criteria utility analysis. This combination aims to improve the accuracy, objectivity, and transparency of the decision-making process. By utilizing both methods, retail companies can establish a more effective, transparent, and data-driven selection system, ensuring that the best employees are chosen based on rational and fair evaluations. The results of the employee selection process using LLSW and MAUT showed that Employee RS ranked first with the highest score of 0.7485, indicating the strongest qualifications compared to the other candidates. Employee LK and Employee ML ranked second and third with scores of 0.6035 and 0.572, respectively, demonstrating solid performance. These selection outcomes can assist companies in recruiting the most suitable workforce for their operational needs and vision, ultimately leading to improved productivity and service quality in the long run. The main contribution of this research is capable of improving accuracy and fairness in employee performance evaluation. This approach reduces the subjectivity that often occurs in conventional assessment processes in the retail sector, as well as providing a basis for transparent and measurable decision-making.
Perbandingan Random Forest dan XGBoost Untuk Prediksi Penjualan Produk E-Commerce Rumah Madu Hayatunnisa, Destaria; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8491

Abstract

Inventory management is one of the main challenges for small and medium enterprises (SMEs), including Rumah Madu in Bandar Lampung, where honey stock levels are often determined based on estimation rather than precise calculation. This study aims to analyze and compare the performance of the Random Forest and XGBoost algorithms in predicting honey sales to achieve more measurable stock management. The dataset consists of 1,699 honey sales transactions that have undergone cleaning, feature transformation, and standardization processes. The variables used include honey type, unit price, day, month, holiday status, and promotion indicators. Modeling was conducted using a time-series split approach, where historical data served as the training set and recent data as the testing set. The evaluation results show that Random Forest achieved an MAE of 24.35, RMSE of 29.04, and R² of -0.9685, while XGBoost achieved an MAE of 25.50, RMSE of 30.58, and R² of -1.1825. The negative R² values indicate that both models were unable to explain data variation optimally, with performance falling below a simple baseline. Nevertheless, the feature importance analysis revealed that unit price and honey type were the dominant factors influencing sales. This study highlights the need for further model development through parameter optimization and improved data quality to enhance prediction accuracy.
Peningkatan Kemampuan Guru SMK Kridawisata di Masa Pandemi Covid-19 Melalui Pengelolaan Sistem Pembelajaran Daring Ahdan, Syaiful; Sucipto, Adi; Priandika, Adhie Thyo; Setyani, Tria; Safira, Wilga; Sari, Kevinda
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 5 No 2 (2021): Volume 5 Nomor 2 Tahun 2021
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v5i2.15591

Abstract

Today's online learning technology has created a new paradigm in the process of implementing learning. Face-to-face activities between teachers and students are no longer a necessity to gain knowledge in school. SMK Kridawisata has adequate facilities and infrastructure to support the learning process such as classrooms and laboratories, but there is no system that is able to apply the learning process in networks that can overcome the problems of the standardized learning process during the Covid-19 pandemic. The solution for implementing online learning systems is expected to increase productivity, especially in the learning process, and to optimize the knowledge and ability of teachers in utilizing online-based learning systems in order to overcome problems that occur when teachers are unable to attend. Online learning systems are built using a learning management system (LMS) platform with the availability of features needed in the learning process online.
Perbandingan Kinerja Model ARIMA dan LSTM dalam Peramalan Harga Crypto Solana (SOL-USD) Berbasis Data Yahoo Finance Wadiyan, Wadiyan; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9444

Abstract

The extreme volatility and non-linear patterns of Solana (SOL) data, driven by its unique consensus mechanism and massive transaction volume, demand accurate forecasting methods to mitigate investment risks. This study compares the statistical method Autoregressive Integrated Moving Average (ARIMA) and Deep Learning Long Short-Term Memory (LSTM) using daily closing price data of SOL-USD from April 2020 to March 2025 obtained from Yahoo Finance. The ARIMA model was developed with optimal parameters (0,1,0), while the LSTM architecture utilized 50 hidden layer units with a 60-day timestep. Evaluation results indicate that the LSTM model significantly outperforms ARIMA, achieving an RMSE of 13.1352 and a MAPE of 6.07% (classified as highly accurate), compared to ARIMA's RMSE of 31.1241 and MAPE of 14.03%. The study concludes that neural network approaches are more effective and adaptive than traditional statistical methods in capturing the highly volatile price dynamics of crypto assets.
Hybrid Music Recommendation System Using K-Means Clustering and Neural Collaborative Filtering for Spotify Playlist Personalization Rastomi Pamungkas; Permata Permata; Rakhmat Dedi Gunawan; Adhie Thyo Priandika
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9181

Abstract

Personalizing music recommendations has become a significant challenge on music streaming platforms such as Spotify due to the vast number of available songs and the limitations of conventional recommendation systems in accurately capturing user preferences. In addition, traditional single-method recommendation approaches often face the cold start problem, which reduces the effectiveness of generated recommendations. Therefore, this study aims to develop and evaluate a hybrid recommendation system that integrates the K-Means Clustering algorithm and Deep Collaborative Filtering based on Neural Matrix Factorization to improve the relevance of music playlist recommendations. The dataset used in this study consists of more than 15,151 Spotify songs obtained from the Spotify dataset available on Kaggle. The dataset was processed through several stages including data inspection, data cleaning, feature selection, and standardization. Audio features used in the analysis include danceability, energy, acousticness, instrumentalness, valence, tempo, and duration. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, resulting in five clusters with a relatively balanced data distribution. The clustering results were then used as the basis for Cluster-Based Filtering to narrow the search space of candidate songs before being processed by the Neural Matrix Factorization model. Performance evaluation was conducted using Hit Ratio at rank 10 and Normalized Discounted Cumulative Gain at rank 10. The proposed model achieved values of 0.1110 and 0.0507, respectively, indicating that the integration of clustering and deep collaborative filtering can improve the effectiveness and personalization of music recommendation systems. This study contributes by proposing a hybrid recommendation framework that integrates clustering-based item grouping with deep collaborative filtering to improve recommendation efficiency and playlist personalization in large-scale music streaming platforms.
Sistem Informasi Administrasi Surat Menyurat Pada Kantor Balai Desa Jatimulyo Jeni Sagita Putri; Adhie Thyo Priandika; Yuri Rahmanto
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 1 No. 1 (2023): Volume 1 Number 1 January 2023
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v1i1.1

Abstract

Kantor Balai Desa Jatimulyo merupakan tempat pelayanan administrasi warga. Sebagai salah satu contoh yaitu pembuatan surat menyurat. Pelayanan surat menyurat ini dilakukan oleh operator atau admin yang bertugas di kantor pelayanan. Namun dalam menjalankan tugasnya, terdapat beberapa kendala yaitu warga yang belum mengetahui alur dalam pembuatan surat, warga yang tidak melengkapi syarat dalam pembuatan surat, dan warga yang harus mengantri dan menunggu dalam pembuatan surat. Untuk memecahkan masalah tersebut, maka dalam penelitian ini dirancang sebuah sistem aplikasi surat menyurat sehingga dapat meningkatkan sistem kerja pegawai Kantor Balai Desa Jatimulyo. Dalam penelitian ini metode yang digunakan yaitu pengembangan metode waterfall dan sistem perancangan untuk penelitian ini menggunakan UML, serta pengujian sistem menggunakan Black Box Testing serta User Acceptance Test. Pengujian pada sistem ini menghasilkan persentase 100% untuk pengujian Black Box Testing dan 89.2% untuk pengujian User Acceptance Test warga serta 94.2% dari User Acceptance Test admin/operator. Hasil yang didapat pada penelitian ini adalah sebuah aplikasi administrasi surat menyurat yang diharapkan dapat mempermudah dan membantu para staff dan warga jatimulyo dalam melakukan proses pembuatan surat
Co-Authors Ade Dwi Putra Ade Surahman Adi Adi Sucipto Adi Sucipto, Adi Aditya Saputra Afitra Tanthowi Agus Irawan Agus Wantoro Ahdan, Syaiful Ahmad Devin Alfitra Tantowi Anas Apririansyah Andi Nurkholis Anggun Dewi Utami Anggun Maylani Anisa Lestari Anissa Anggraini Annisa Anggraini An’ars, M. Ghufroni Ari Najeri Ari Sulistiyawati Arif Budiman Aryani, Venty A’yun, M Qurrota Bagas Aditama Bayu Pratama Bustanul Ulum Dedi Darwis Dedi Irawan Dellys Okta Wibowo Dina Ros Muryana Donaya Pasha Donaya Pasha Doni Riswanda Doni Riswanda Dwi Rahma Sari Dyah Ayu Megawaty Ebi Supriyadi Edison, Arif Rahman Edvan Agus Pratama Eky Khoiril Ulama Erliyan Redy Susanto Fazri Syanofri Fenty ariany Fitratullah, M. Fuad Surya Mawinar Gantar Galang Toyyibah Gunawan, Rakhmat Dedi Harry Anggono Hayatunnisa, Destaria Heni Sulistiani Imam Asyrofi Alfarisi Imroatun Qoniah Intan Anggrenia Isnain, Auliya Rahman Jeni Sagita Jeni Sagita Putri Johansyah Johansyah josua Armando silalahi Koeswara, Wawan Linda Fatmawati Lutfy, Azza’zunda Choibar Meiwidia Seftiana Mico Fahrizal Mirza Wijaya Putra Muhamad Amirudin Muhammad Alba Muhammad Indigo Muhammad Rahadiyan Bagaskara Muhaqiqin muhaqiqin Muhtad Fadly Ningsih, Ristia Octaviansyah, A. Ferico Parjito Parjito Pasaribu, A. Ferico Octaviansyah Permata Permata Permata Permata Permata, Permata Prabowo, Fransiskus Wahyu Sandy Prasetyo Bella Ramadhanu Prastowo, Agung Tri Putra, Farhan Nopransyah Rahmat Dedi Gunawan Rakhmad Dedi Gunawan Rakhmat Dedi Gunawan Rastomi Pamungkas Riduan Napianto Ridwan Janata Rifaldo, Setiawan Riski Etien Malovi Rizki Putra Utama Rohaniah Rohaniah Rohmat Indra Borman Rosella, Rosella S. Samsugi Safira, Wilga Salsabila Indriyani Sanriomi Sintaro Sari, Kevinda Setiawansyah Setiawansyah Setiawansyah Setiawansyah Setyani, Tria Sherly Octavia Simamora, Parningotan Sinta Agita Sari Stevan Corry Polanco suaidah suaidah Temi Ardiansah Tia Nanda Pratiwi Tien Yulianti Very Hendra Saputra Wadiyan, Wadiyan Wahyu Widiantoro Wahyudi, Agung Deni Wahyuni, Dita Septia Wilga Safira Yogi Suwarno YOHANA TRI UTAMI, YOHANA TRI Yuri Rahmanto Yusma Indonesian Yusran, Muhamad