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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat
Martanto
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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PKM Penguatan Kapasitas Sdm Desa Pelatihan Manajemen Dan Keterampilan Teknis Untuk Pembangunan Lokal Wahyudin, Edi; Martanto; Dikananda, Fatihanursari; Rano; Nasakh
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 5 : Juni (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The Community Service Program (PKM) titled "Strengthening Village Human Resources: Management and Technical Skills Training for Local Development" aims to address the limitations in human resource capacity that often hinder development in villages. This program is designed to enhance the managerial skills of village officials and develop the technical skills of residents in agriculture, crafts, and information technology. Implementation begins with needs identification through surveys and interviews, followed by the design of an appropriate curriculum, and the delivery of training by experts and practitioners. The results of this program show a significant improvement in the managerial abilities of village officials and the technical skills of residents, contributing to the development of local businesses and the village economy. Evaluations also indicate an increased awareness of the importance of strengthening human resource capacity for sustainable development, making this program a potential model for other villages.
ANALISIS INTERNET MENGGUNAKAN PARAMATER QUALITY OF SERVICE PADA ALFAMART TUPAREV 70 Satria Turangga; Martanto; Yudhistira Arie Wijaya
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 6 No. 1 (2022): JATI Vol. 6 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v6i1.4693

Abstract

Penggunaan internet bagi karyawan merupakan sebuah kebutuhan untuk menunjang pekerjaan agar dapat diselesaikan. Adapun fasilitas internet yang diberikan oleh PT Sumber Alfaria Trijaya Tbk kepada toko-tokonya yang khususnya Alfamart Tuparev 70. Hal ini terlihat dari seringnya gangguan koneksi. Penelitian ini bertujuan untuk mengukur performa koneksi internet yang ada di Alfamart Tuparev 70. Metode yang digunakan adalah Quality of Service (QoS). QoS dibutuhkan untuk menghitung parameter yang nanti dapat menentukan kualitas dari sebuah jaringan internet. Tahapan dalam penelitian ini merekam trafik jaringan menggunakan wireshark kemudian menghitung parameter yang digunakan. Nilai QoS yang diperoleh pada saat upload data jam 08.00-12.05 memperoleh nilai persentase throughput yaitu 31% dengan indek 1 “JELEK”, delay 9,444 ms dengan indek 4 “SANGAT BAGUS”, jitter 8,444 ms dengan indeks 3 “BAGUS” dan packet loss 0% dengan indeks 4 “SANGAT BAGUS”. Nilai QoS yang diperoleh pada saat download data jam 15.00-19.05 bahwa nilai persentase throughput 132% dengan indek 4 “SANGAT BAGUS”, delay 14,052 dengan indek 4 “SANGAT BAGUS”, jitter 13,052 dengan indek 3 “BAGUS” dan packet loss mendapatkan indeks 4 “SANGAT BAGUS”. Dapat disimpulkan bahwa pada saat upload maupun download, koneksi internet pada Alfamart Tuparev 70 masih layak digunakan dan sudah memenuhi standart TIPHON.
KLATERISASI DATA PENDUDUK BERDASARKAN PEKERJAAN MENGGUNAKAN METODE K-MEANS PADA WILAYAH JAWA BARAT Jannah, Eka Roehatul; Martanto
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 2 (2024): JATI Vol. 8 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i2.9055

Abstract

Perkembangan teknologi merupakan peluang yang tepat memperoleh data dengan lebih efektif dan efisien. Data mining adalah salah satu komponen dalam proses Knowledge Discovery in Databases (KDD). KDD adalah suatu rangkaian proses yang bertujuan menemukan informasi yang bermanfaat dari sumber data dalam database. Permasalahan dalam penelitian ini, bagaimana jika Metode K-Means mungkin tidak sesuai untuk mengelompokkan data penduduk berdasarkan pekerjaan?. Penelitian ini bertujuan untuk mengidentifikasi pola pekerjaan penduduk wilayah Jawa Barat dan membentuk kelompok pekerjaan yang serupa. Melalui metode K-Means, akan memungkinkan saya untuk mengelompokkan penduduk Jawa Barat berdasarkan jenis pekerjaan mereka menggunakan tahapan KDD. Dengan tahapan KDD kita dapat dengan mudah melihat data penduduk berdasarkan pekerjaan dari tahun 2011-2023. Dapat diambil kesimpulan bahwa penduduk yang bekerja dengan nilai tertinggi adalah pada Cluster 3 yang ditandai dengan warna biru (tinggi) berjumlah 151 items, untuk data pekerjaan dengan nilai sedang berada pada Cluster 2 yang ditandai dengan warna oranye (sedang) berjumlah 100 items, dan ntuk penjualan dengan nilai terendah yaitu pada Cluster 0 dan Cluster 1 yang ditandai dengan warna hijau dan hitam (rendah) dengan jumlah yang sama yaitu 50 items. Hasil percobaan yang dilakukan pada data penduduk berdasarkan pekerjaan menggunakan metode DBI (Davies Bouldin Index), menghasilkan nilai K terbaik pada cluster 4 yaitu 0,262.
Bibliometrik Analysis: Konten Video Untuk Meningkatkan Daya Tarik Pariwisata Arif Rinaldi Dikananda; Dadang Sudrajat; Fatihanursari Dikananda; Rudi Kurniawan; Martanto
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The use of video content as a marketing tool in the tourism industry has seen a significant increase in recent years. This research aims to explore and develop effective video content strategies in increasing tourism appeal and influencing tourists' decisions to visit certain destinations. Research methods include bibliometric analysis of video content used in tourism marketing, as well as experiments to test the effectiveness of various video content strategies. The results of the study show that the characteristics of travel vlogs that include personal narratives, attractive visuals, and relevant information can increase user travel intentions. Additionally, audience engagement through short videos has proven to be a key factor in increasing travel interest. This research makes a new contribution in understanding the role of video content in tourism marketing and developing a video marketing strategy model that can be applied by the tourism industry to increase the attractiveness of tourist destinations. By utilizing the results of this study, the tourism industry can optimize the use of video content to reach a wider audience and increase positive perceptions of tourist destinations.
Analisis Internet Network Performance Menggunakan Parameter Quality of Service Pada Jaringan STMIK IKMI Cirebon Martanto; Dian Ade Kurnia; Fathurrohman; Irfan Ali; Yudhistira Arie Wijaya
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The use of the internet for employees is a crucial need to support the completion of their work. STMIK IKMI Cirebon also provides internet facilities for its employees. However, the internet facilities provided are not yet optimal, as evidenced by frequent connection disruptions. This study aims to measure the performance of the internet connection at STMIK IKMI Cirebon. The method used is Quality of Service (QoS), which is a method to assess how well the installed network functions and its ability to define the attributes of the network services provided. QoS is necessary to calculate the parameters that determine the quality of an internet network. The steps in this study include recording network traffic using Wireshark, followed by calculating parameters such as bandwidth, packet loss, delay, throughput, and jitter. The study results indicate that during data upload from 08:00 to 12:05 at STMIK IKMI Cirebon, the throughput percentage achieved was 31% with an index of 1 "POOR," delay was 9.444 ms with an index of 4 "VERY GOOD," jitter was 8.444 ms with an index of 3 "GOOD," and packet loss was 0% with an index of 4 "VERY GOOD." During data download from 15:00 to 19:05, the throughput percentage achieved was 132% with an index of 4 "VERY GOOD," delay was 14.052 ms with an index of 4 "VERY GOOD," jitter was 13.052 ms with an index of 3 "GOOD," and packet loss received an index of 4 "VERY GOOD." Based on these results, it can be concluded that these values have met the TIPHON standard for both upload and download, indicating that the internet connection at STMIK IKMI Cirebon is still suitable for use
ANALISIS SEGMENTASI PELANGGAN VOUCHER WIFI DENGAN METODE K-MEANS Fahmi Naufal; Martanto; Arif Rinaldi Dikananda; Rohman, Dede
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.5169

Abstract

AirNet Teknologi, yang bergerak di bidang konsultasi komputer dan penyedia layanan internet WiFi, melakukan penelitian untuk menerapkan teknik clustering menggunakan algoritma K-Means dalam menganalisis data penjualan voucher WiFi di cabang Talun dari 31 Oktober 2023 hingga 31 April 2024. Penelitian ini bertujuan untuk mengidentifikasi pola pembelian pelanggan berdasarkan jenis paket, durasi penggunaan, dan harga, guna meningkatkan strategi pemasaran perusahaan. Dataset yang dianalisis mencakup detail transaksi seperti tanggal, jenis produk, durasi penggunaan, dan total pembayaran. Proses analisis dilakukan menggunakan RapidMiner Studio dengan tahapan Knowledge Discovery in Database (KDD), termasuk seleksi data, praproses, dan evaluasi hasil clustering menggunakan Davies-Bouldin Index (DBI). Hasil menunjukkan jumlah klaster optimal adalah K = 6 dengan nilai DBI 0.182, menandakan kualitas clustering yang baik. Pelanggan dikelompokkan ke dalam enam klaster dengan karakteristik berbeda, yang dapat digunakan untuk menargetkan promosi dan program loyalitas. Penelitian ini menekankan pentingnya analisis data dalam pengambilan keputusan strategis, memungkinkan AirNet Teknologi untuk menyusun strategi pemasaran yang lebih efektif, meningkatkan kepuasan pelanggan, dan memperkuat posisinya di pasar layanan WiFi.
Comparison of Sentiment Analysis Models Enhanced by Naïve Bayes and Support Vector Machine Algorithms on Mobile Banking BRImo Reviews Ramadan, Muhamad Firly; Martanto; Dikananda, Arif Rinaldi; Rifa'i, Ahmad
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.732

Abstract

This study compares the effectiveness of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying user sentiment regarding the BRImo application. User reviews were obtained from the Google Play Store platform and underwent a text preprocessing stage to clean and prepare the data. Subsequently, the SVM and Naïve Bayes algorithms were applied for sentiment analysis, using evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that SVM achieved a training accuracy of 95.67% and a testing accuracy of 83.11%, with its best performance on positive sentiment (precision 92.26%, recall 91.79%, F1-score 92.02%) and moderate performance on negative sentiment (precision 62.81%, recall 62.81%, F1-score 62.81%). Meanwhile, Naïve Bayes recorded a training accuracy of 95.23% and a testing accuracy of 82.77%, with its highest performance on positive sentiment (precision 90.12%, recall 93.38%, F1-score 91.72%) but lower performance on negative sentiment (precision 65.07%, recall 60.06%, F1-score 62.46%). In terms of sentiment distribution, SVM was more effective in handling sentiment variations, particularly in detecting negative and neutral sentiments. These findings indicate that SVM outperforms Naïve Bayes in sentiment analysis of user reviews for the BRImo application.
Improving the School Type Clustering Model on the Foundation Using the K-Means Algorithm (Case Study: Kebon Kelapa Al-Ma'rifah, Cirebon Regency) Hanifah Nur Aulia; Martanto; Arif Rinaldi Dikananda; Mulyawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.739

Abstract

This study aims to improve the school type grouping model at the Kebon Kelapa Al-Ma'rifah Foundation, Cirebon Regency, using the K-Means algorithm. Data-based grouping is very important in supporting efficient education management, especially in environments that have various types of schools such as Madrasah Aliyah (MA), Vocational High School (SMK), Madrasah Tsanawiyah (MTs), and Madrasah Ibtidaiyah (MI). The data used comes from the New Student Registration (PPDB) dataset for the 2023–2024 school year, with demographic attributes such as name, place of birth, gender, and time of school entry. The evaluation of clustering quality was carried out using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The results show that the optimal number of clusters is K=5 with the lowest DBI value of 0.201, which results in compact and well-separated clusters. The implementation of the K-Means algorithm helps the foundation understand the distribution pattern of students based on attributes such as gender, region, and entry time. This research provides practical benefits, including more targeted resource allocation, improved quality of education, and efficiency in school management. In addition, this research contributes to the development of data mining models in the education sector and opens up opportunities for the exploration of additional attributes such as academic achievement and socioeconomic conditions. Further research is suggested to use alternative algorithms such as K-Medoids or DBSCAN.
Support Vector Regression to Improve Ethereum Price Prediction for Trading Strategies Muhamad Abdul Fatah; Martanto; Dikananda, Arif Rinaldi; Rifai, Ahmad
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.740

Abstract

Predicting erratic assets like Ethereum is difficult in the dynamic cryptocurrency market. This study uses an enhanced Support Vector Regression (SVR) algorithm to create a daily price prediction model for Ethereum. Yahoo Finance provided the data, which was preprocessed to include missing value cleaning, normalization, and feature extraction of Moving Average (MA) and Exponential Moving Average (EMA). The data was collected between August 4, 2019 and August 4, 2024. An ideal combination was obtained by parameter optimization with GridSearchCV: gamma scale, linear kernel, epsilon of 1, and C of 100. The model performed well, as evidenced by its R2 of 0.9985 and MSE of 2137.97. The model's reliability in predicting Ethereum's price movement patterns was validated via prediction graphs. A 30-day forecast indicated a stable trend, with prices slightly decreasing from $2921.31 on January 1, 2025, to $2919.83 on January 31, 2025. These results highlight the importance of data preprocessing and parameter optimization in enhancing SVR model performance.
Naive Bayes Algorithm to Enhance Sentiment Analysis of Coursera Application Reviews on Google Play Store Masdarul Rizqi; Martanto; Arif Rinaldi Dikanda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.758

Abstract

Coursera is an online learning platform that provides various courses and certifications. This study aims to analyze user perceptions of the Coursera application after the reviews are translated into Indonesian, identify factors that influence positive and negative sentiment, and activate the effectiveness of the Naive Bayes algorithm in classifying review sentiment. The method used is Knowledge Discovery in Databases (KDD), with stages of data collection, preprocessing, and sentiment analysis using Naive Bayes. The results of the study show that the translation of reviews does not change the essence of user perception. Analysis of key words reveals positive experiences such as "kursus", "berguna", and "terima kasih", as well as criticism related to application performance. Factors such as price, content, and user experience play an important role in positive sentiment, while technical issues are the main cause of negative sentiment. The Naive Bayes model shows high accuracy with an accuracy value of 83.62%, precision of 83.34%, recall of 87%, and F1-score of 85.2%. These results indicate that the Naive Bayes algorithm is effective in analyzing sentiment of Coursera application user reviews. Further research is recommended to explore other algorithms or expand the analysis by considering additional factors that can influence user sentiment
Co-Authors Ahmad Rifai Ahmad Rifai Ahmad Rifa’i Arif Budi Setiawan Arif Rifaldi Dikananda Arif Rinaldi Dikananda Arif Rinaldi Dikanda Artoti, Azzahra Rizky Ayu Febrian Lesmana, Alfira Azzahra Moudy Fajria Bagus Hermawan Candra, Candra Rahmawati Dadang Sudrajat Dede Rohman Dian Ade Kurnia Dikananda, Arif Rinaldi Dikananda, Fatihanursari Fahmi Naufal Fatihanursari Dikananda Hanifah Nur Aulia Hayati, Umi Hery Widijanto Ica, Ica Pandia Ika Riantika Irfan Ali Jannah, Eka Roehatul Jayawarsa, A.A. Ketut Laylatunna'imah Masdarul Rizqi Muhamad Abdul Fatah Muhammad Hilman Mulyawan Nasakh Nuraeni Pamungkas, Vicky Rai Fatkaozi, Ahmad Ramadan, Muhamad Firly Rano Renaldi , Betran Ridho Nugraha Rifa'i, Ahmad Rohayati, Eti Rohman, Dede Rudi Kurniawan Satria Turangga Wahyudin, Edi Wiratriyana Yudhistira Arie Wijaya