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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat CARADDE: Jurnal Pengabdian Kepada Masyarakat IJAIT (International Journal of Applied Information Technology) Prosiding Konferensi Nasional PKM-CSR Indonesian Community Journal Literasi: Jurnal Pengabdian Masyarakat dan Inovasi eProceedings of Applied Science eProceedings of Engineering Jurnal Pengabdian Masyarakat Bangsa
Dedy Rahman Wijaya
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Simple Machine Learning Architecture as a Service Tora Fahrudin; Dedy Rahman Wijaya
IJAIT (International Journal of Applied Information Technology) Vol 07 No 01 (May 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i01.5991

Abstract

Machine learning (ML) development starting in the 1950s, has shown significant progress. Various fields have used machine learning as an information system element that is useful in assisting data processing, personalization, prediction, and performing anomaly detection of occurring transactions. Along with developments, cloud-based machine learning technology is becoming the choice for ease of implementation and connectivity with various other technology platforms. This paper proposes a machine learning architecture as a service (MLaaS) implemented in a case study of a gender prediction model based on height and weight. The results show that the MLaaS architecture is straightforward to implement and fits the needs of various access environments and the ease of updating models centrally. Our gender prediction model achieved 91.78% in the precision, recall, and F1-score, 91.8% in specificity and NPV, and 91.79% in accuracy.
Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection Pramuko Aji; Dedy Rahman Wijaya; Elis Hernawati; Sherla Yualinda; Sherli Yualinda; Muhammad Akbar Haikal Frasanta; Rathimala Kannan
IJAIT (International Journal of Applied Information Technology) Vol 7 No 02 (2023): Vol 07 No 02 (November 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i02.5374

Abstract

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.
Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection Pramuko Aji; Dedy Rahman Wijaya; Elis Hernawati; Sherla Yualinda; Sherli Yualinda; Muhammad Akbar Haikal Frasanta; Rathimala Kannan
IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i02.5374

Abstract

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.
Peningkatan Layanan Keuangan Sekolah dengan Aplikasi Cashless Payment (Studi Kasus SMK Pariwisata Telkom Bandung) Rima Aditya, Bayu; Iradianty, Aldilla; Gartina, Inne; Rahayu, Sri; Prabawa Kusuma, Guntur; Rahman Wijaya, Dedy; Komala Sari, Siska
Literasi Jurnal Pengabdian Masyarakat dan Inovasi Vol 3 No 1 (2023)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang Jl. Rangga Sentap, Dalong Sukaharja, Ketapang 78813. Telp. (0534) 3030686 Kalimantan Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/literasi.v3i1.1265

Abstract

SMK Pariwisata Telkom Bandung is a school that is under the auspices of the Yayasan Pendidikan Telkom and is a partner in this community service program. The problem faced by partners is the limited level of knowledge and skills of schools in using information and communication technology (ICT) which causes a lack of innovation implemented in school, especially in the financial function. The solution offered in the program is to provide training and assistance in terms of digitalization school financial services based on cashless payment to support innovation in school financial services. The results of participant feedback have shown that the program can positively provide digital literacy education related to the recent financial technology. Therefore, this program contributes to strengthening and improving the ability to manage school financial services. In addition, this community service activity also produces an appropriate technology in the form of a cashless payment-based school finance application.
Pemanfaatan Aplikasi Pembayaran SPP Berbasis Website Pada SMK Pariwisata Telkom Bandung Sebagai Pendukung Cashless Payment di Sekolah Aditya, Bayu Rima; Wijaya, Dedy Rahman; Permadi, Aditya
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 6 (2023): INOVASI PERGURUAN TINGGI & PERAN DUNIA INDUSTRI DALAM PENGUATAN EKOSISTEM DIGITAL & EK
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v6i0.1882

Abstract

Walaupun sistem pembayaran SPP pada SMK Pariwisata Telkom Bandung sudah mulai menerapkan cara pembayaran melalui transfer bank, namun proses pencatatan keuangannya masih dilakukan secara manual berupa jurnal harian yang kemudian direkap ke dalam pembukuan. Oleh karena itu, kegiatan pengabdian masyarakat ini bertujuan untuk menerapkan aplikasi pembayaran SPP di sekolah berbasis website. Berdasarkan hasil evaluasi kegiatan, didapat bahwa aplikasi pembayaran SPP berbasis website pada SMK Pariwisata Telkom Bandung telah membantu digitalisasi proses penyimpanan, pencarian dan pembuatan laporan pembayaran SPP siswa maupun dokumen terkait. Dengan kata lain, kegiatan pengabdian masyarakat ini telah menghasikan teknologi tepat guna berupa aplikasi pembayaran SPP berbasis website.
Pelatihan Visualisasi Data Kebangkrutan Perusahaan untuk Guru SMKN 3 Bandung Fahrudin, Tora; Asniar, Asniar; Wijaya, Dedy Rahman; Ardiansyah, Fajri
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 4 (2024): Juni
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i4.946

Abstract

Salah satu hal esensial dalam bisnis adalah persoalan keuangan. Menjaga Kesehatan keuangan perusahaan sangat penting karena memiliki dampak langsung pada kelangsungan hidup, pertumbuhan, dan keberlanjutan bisnis. Di era sekarang penerapan Data Science berbasis Machine Learning telah mengalami kemajuan signifikan. Tren kebutuhan tenaga kerja untuk Data Science khususnya di bidang keuangan mencerminkan perkembangan cepat. Telkom University sebagai universitas yang mengedepankan teknologi informasi untuk penyelesaian permasalahan-permasalah di masyarakat dituntut dapat memberikan peran dalam adopsi-adopsi teknologi informasi kepada masyarakat sekitar, di antaranya melalui pelatihan visualisasi data kebangkrutan perusahaan. Hal ini diharapkan dapat memberikan inspirasi dan pengetahuan lanjut mengenai konsep maupun implementasi tools data science kepada para guru SMK Negeri 3 Bandung. Melalui  pelatihan dan transfer teknologi tepat guna ini diharapkan dapat menjadi solusi terhadap kebutuhan literasi akan ilmu data science bagi guru maupun siswa smk. Dengan adanya pelatihan ini, pemahaman mengenai konsep ilmu data sudah mulai dapat diterapkan di lingkungan SMK. Hal ini terlihat dari hasil feedback yang diperoleh pada saat akhir pelatihan tools visualisasi kebangkrutan perusahaan dilakukan, dimana 89.47% peserta menyatakan materi sesuai dengan kebutuhan masyarakat, 78.95% peserta menyatakan waktu yang disediakan cukup memadai, 94.75% peserta menyatakan materi yang disajikan jelas dan mudah dipahami.
Pembangunan Website Sebagai Sarana Promosi Desa Wisata Di Desa Kopeng Kabupaten Semarang Marchiningrum, Anranur Uwaisy; Kusuma, Guntur Prabawa; Wijaya, Dedy Rahman; Damanik, Junia Putri; Muhammad, Rafi; Narwin, Agung Isra; Gunadi, Gagah Aji
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 7 (2024): September
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i7.1226

Abstract

Desa wisata memiliki potensi yang signifikan untuk meningkatkan perekonomian lokal, namun penyebaran informasi mengenai daya tarik wisata di Desa Kopeng sering kali terbatas pada promosi tradisional seperti dari mulut ke mulut. Kelompok Sadar Wisata (Pokdarwis) di Desa Kopeng telah mengidentifikasi berbagai potensi wisata alam, pertanian, perkebunan, dan budaya yang ada di desa tersebut. Akan tetapi, sejak pandemi COVID-19, jumlah kunjungan wisatawan ke Desa Kopeng masih sedikit sehingga belum kembali seperti semula. Oleh karena itu, salah satu upaya untuk mengatasi tantangan ini adalah dengan melakukan pengembangan dan pemanfaatan website. Website yang terhubung dengan internet dapat menyebarkan informasi secara cepat dan luas ke seluruh dunia, serta berfungsi sebagai "landing page" yang mengintegrasikan berbagai informasi yang tersebar di media sosial. Pengelolaan website oleh tenaga lokal sangat penting agar promosi yang dilakukan efektif dan dapat berkelanjutan. Program pengabdian masyarakat ini bertujuan untuk membangun website yang dapat digunakan sebagai alat promosi Desa Wisata Kopeng, serta memberikan pelatihan dan pendampingan kepada tenaga lokal dalam pengelolaan website tersebut. Kegiatan ini merupakan hasil kolaborasi antara Fakultas Ilmu Terapan dan Fakultas Informatika, dengan luaran berupa sebuah website yang mampu menjangkau calon wisatawan domestik maupun mancanegara.
Pelatihan Pemodelan Data untuk Prediksi Kebangkrutan Perusahaan bagi Guru SMKN 3 Bandung Fahrudin, Tora; Asniar, Asniar; Wijaya, Dedy Rahman; Ardiansyah, Fajri
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 11 (2025): Januari
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i11.1949

Abstract

Data Science yang berbasis pada machine learning (ML) telah mengalami perkembangan signifikan di era teknologi informasi saat ini. Dengan kemajuan yang pesat dalam integrasi teknologi dan analitika data untuk mendukung pengambilan keputusan di sektor keuangan, terdapat peningkatan tren kebutuhan tenaga kerja untuk data science, khususnya dalam bidang keuangan. Sebagai institusi pendidikan yang memanfaatkan teknologi informasi untuk menyelesaikan permasalahan, Universitas Telkom memiliki tanggung jawab untuk turut serta dalam mengadopsi dan mentransfer teknologi informasi kepada masyarakat. Salah satu contoh dari upaya ini adalah pelatihan pemodelan data untuk memprediksi kebangkrutan perusahaan bagi para guru di SMKN 3 Bandung. Pelatihan dan transfer teknologi yang tepat ini diharapkan dapat memberikan pengayaan ilmu kepada guru dan siswa SMK dalam pemahaman dan penerapan data science. Hal ini dibuktikan dari tanggapan peserta selama pelatihan pemodelan data untuk memprediksi kebangkrutan perusahaan. Seratus persen peserta menyatakan bahwa materi yang disampaikan sesuai dengan kebutuhan masyarakat, sembilan puluh lima persen peserta menyatakan bahwa waktu pelaksanaan kegiatan relatif sesuai dan cukup memadai, dan delapan puluh sembilan persen peserta menyatakan bahwa materi atau kegiatan yang disajikan jelas serta mudah dipahami. Diharapkan pelatihan ini dapat melatih pemikiran kritis guru serta mendorong mereka untuk memasukkan materi data science ini ke dalam kurikulum di sekolah menengah kejuruan.
Automated Tuna Freshness Assessment via Gas Sensors and Machine Learning Algorithms Pratama , Nyoman Raflly; Novamizanti, Ledya; Wijaya, Dedy Rahman
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ensuring the safety and health of fish products is crucial for public health, with tuna being Indonesia's second most popular fishery product. Tuna freshness is a key indicator of seafood safety, directly impacting both nutritional quality and contamination risk. This study compares the K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM) algorithms to assess and classify tuna freshness, offering an accurate and efficient approach. A machine learning model categorized Tuna freshness based on the gases emitted, utilizing a dataset of 58,389 records. Gas changes were detected using the MQ-135, MQ-9, and MQ-2 sensors, which are highly sensitive to gases like ammonia, methane, and alcohol, commonly associated with spoilage. The KNN, Naive Bayes, and SVM algorithms were then applied to classify the sensor data. KNN and SVM achieved an accuracy of 99%, while Naive Bayes reached 90%. The high accuracy of these methods highlights their potential as practical tools for the fishing industry, enabling suppliers and retailers to assess tuna freshness more effectively. This method could significantly improve consumer safety by ensuring only high-quality, fresh products reach the market. Additionally, automation offers substantial time savings, facilitates faster decision-making, and reduces reliance on manual inspections prone to human error. Keywords—tuna, classification, gas sensor, machine learning
Comparison of k-NN and Naive Bayes Algorithms for Classifying Mackerel Tuna Freshness For Real-Time Classification Using Gas Sensors Setyagraha , Muhammad Rafi Mahfuz; Novamizanti, Ledya; Wijaya, Dedy Rahman
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The large production and consumption of mackerel tuna in Indonesia reflect its importance as a local staple and a valuable export product contributing to the nation's economy. Mackerel tuna is prized for its nutritional content and affordability, making it a crucial part of the diet for many Indonesians. Ensuring the freshness and quality of this high-demand product is essential. This study introduces a machine-learning approach to detect fish freshness by analyzing gases emitted during spoilage, utilizing MQ-2, MQ-9, and MQ-135 gas sensors. The data were processed using the k-Nearest Neighbors (k-NN) and Naive Bayes algorithms, both achieving accuracy rates near 100%. These findings highlight the system’s potential to enhance quality control in Indonesia’s fishery industry by offering an efficient and reliable method for assessing fish freshness. Keywords—classification, machine learning, tuna, gas sensor
Co-Authors , Inne Gartina Aditya Permadi, Aditya Aditya, Bayu R Aldilla Iradianty Ardiansyah, Fajri Asniar Asniar Astri Wulandari Bayu Rima Aditya Bethani Suryawardani Damanik, Junia Putri Dendi Gusnadi Elis Hernawati Fathya Ariyani Fazri, Irfan Moch Gartina, Inne Gunadi, Gagah Aji Gunawan, Tedi Guntur Prabawa Kusuma Hermawan, Laksamana Mikhail Heru Nugroho Inne Gartina Irfan Moch Fazri Ledya Novamizanti Marcelino, Dandy Marchiningrum, Anranur Uwaisy Muhammad Akbar Haikal Frasanta Muhammad Nezar Mahardika Muhammad, Rafi Narwin, Agung Isra Pramuko Aji Pratama , Nyoman Raflly Rathimala Kannan Riyanarto Sarno Setyagraha , Muhammad Rafi Mahfuz Sherla Yualinda Sherli Yualinda Siska Komala Sari SRI RAHAYU Sri Rahayu Suryana Suryana Suryatiningsih Tora Fahrudin Wawa Wikusna