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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Darmabakti Cendekia: Journal of Community Service and Engagements Jurnal Layanan Masyarakat (Journal of Public Service)
Rachman Sinatriya Marjianto
D3 Sistem Informasi Fakultas Vokasi Universitas Airlangga
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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PREDIKSI PENENTUAN PENERIMA BEASISWA DENGAN METODE KNEAREST NEIGHBOURS (Studi Kasus: Program Studi Sistem Informasi Fakultas Vokasi Universitas Airlangga) Nasa Zata Dina; Rachman Sinatriya Marjianto
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 2, No 2 (2018): InfoTekJar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v2i2.269

Abstract

Sistem pendukung keputusan berupa prediksi penentuan penerima beasiswa online diharapkan dapat membantu dalam menentukan mahasiswa calon penerima beasiswa. Pada penelitian ini difokuskan pada studi kasus Program Studi Sistem Informasi Fakultas Vokasi, Universitas Airlangga. Banyaknya jumlah beasiswa yang masuk ke fakultas mengakibatkan staf  kemahasiswaan kewalahan karena jumlah calon penerima beasiswa sangat banyak sedangkan beasiswa yang ditawarkan terbatas. Kuota beasiswa sendiri juga diberikan pada tiap program studi berdasarkan banyaknya jumlah mahasiswa pada tiap angkatan. Karena proses administrasi serba manual maka petugas kemahasiswaan kesulitan dalam menentukan penerima dari beasiswa yang ada pada tiap semesternya. Akibatnya beberapa beasiswa peruntukkannya belum tepat sasaran sehingga terkadang ada beberapa mahasiswa yang menerima beasiswa ganda akibat kelalaian dalam penentuan penerima beasiswa. Oleh karena permasalahan tersebut maka sistem pendukung berupa prediksi penentuan penerima beasiswa sangat dibutuhkan untuk saat ini demi membangun sistem terkomputerisasi yang terstruktur di lingkungan Program Studi Sistem Informasi Fakultas Vokasi, Universitas Airlangga. Metode penelitian yang digunakan mencakup pengumpulan data, analisis sistem, desain system, implementasi sistem dan evaluasi dan pengujian sistem. Sedangkan algoritma yang digunakan adalah Algoritma  KNearest Neighbours. Nilai parameter K yang digunakan adalah 2. Indeks Kemiskinan dan Indeks Prestasi Kumulatif  merupakan variabel yang diukur. Nilai akurasi yang dihasilkan oleh algoritma KNearest Neighbours dengan nilai K= 2 yaitu sebesar 83,4%.
DIGITAL ENTREPRENEURSHIP TRAINING FOR MICRO, SMALL AND MEDIUM ENTERPRISES (MSMES) FOR TYPICAL CULINARY OF TUBAN REGENCY Novyandri Taufik Bahtera; Rachman Sinatriya Marjianto; Moh. Darus Salam
Darmabakti Cendekia: Journal of Community Service and Engagements Vol. 4 No. 1 (2022): JUNE 2022
Publisher : Faculty of Vocational Studies, Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (604.955 KB) | DOI: 10.20473/dc.V4.I1.2022.31-36

Abstract

Background: With the presence of Micro, Small and Medium Enterprises (MSMEs), it can increase economic growth, absorption of labor and reduce poverty in Indonesia, especially in Tuban Regency. MSME's in Tuban Regency continues to increase from year to year. The problems they face are in the form of narrow marketing and a lack of distribution channels for goods. In addition, MSME's players are also faced with the problem of being unable to take good photos of their products so that the product photos are not attractive to customers. MSME's players are also faced with the problem of not properly recording business financial reports because they are still mixed with personal matters. Objective: This activity aims to increase the capacity of MSME entrepreneurs through training in the fields of digital finance, digital marketing and digital photography. Methods: The method applied is in the form of interactive training, discussion, and practice, by: 1) implementing digital finance training using the BukuWarung application, 2) Participants practicing strategies in developing digital marketing by utilizing technological advances such as social media and marketplace platforms. And 3) Participants practice taking photos of their MSME's products by paying attention to the aesthetics of the right photos. Results: MSME's entrepreneurs have increased abilities and insights in applying financial bookkeeping, marketing and drawing product images by utilizing the latest technology. Conclusion: Community service partners are able to increase their business competitiveness throughinsight and expertise in financial accounting, marketing and digital photography.
Otomasi Layanan Cetak Pada Sistem Cloud Printing Menggunakan CUPS Rachman Sinatriya Marjianto; Nanda Ridho Wirowicaksono
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 2 No. 1 (2019): Jutikomp Volume 2 Nomor 1 April 2019
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v2i1.441

Abstract

Mencetak dokumen telah menjadi sebuah kebutuhan dari seluruh individu atau organisasi, terutama civitas akademik. Survey yang dilakukan kepada 100 mahasiswa sebagai responden untuk melihat kebutuhan layanan cetak di dalam lingkungan Fakultas Vokasi Universitas Airlangga menunjukan tingginya kebutuhan layanan cetak dan hal tersebut belum terpenuhi. Model Kano digunakan untuk melakukan survey kebutuhan fitur layanan cetak dan hasilnya menunjukan bahwa layanan cetak yang ditawarkan secara online dapat memberikan dampak positif bagi layanan cetak tersebut. Organisasi yang akan memberikan layanan cetak ini adalah himpunan mahasiswa dimana ketersediaan waktunya terbatas karena tingginya jam perkuliahan. Oleh sebab itu diperlukan sebuah sistem layanan cetak yang bekerja secara otonom. Tiga buah background proses dikembangkan untuk menjalankan sistem layanan cetak otomatis dimana komunikasi dengan printer-printer yang tersedia menggunakan CUPS.
WEBSITE ONLINE PORTAL DESIGN OF SAJEN VILLAGE, PACET, MOJOKERTO Alifian Sukma; Rachman Sinatriya Marjianto; Tesa Eranti Putri
Jurnal Layanan Masyarakat Vol. 7 No. 1 (2023): JURNAL LAYANAN MASYARAKAT
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jlm.v7i1.2023.82-90

Abstract

Perkembangan teknologi yang semakin pesat memungkinkan untuk memperoleh berbagai macam informasi dimanapun dan kapanpun ia berada. Hal tersebut tentu sangat mempengaruhi pola konsumsi masyarakat baik di Indonesia maupun dunia. Indonesia sebagai salah satu negara berkembang dengan potensinya sebagai salah satu destinasi wisata baik lokal maupun mancanegara, tentu sangat bergantung dengan pola konsumsi yang ada pada era informasi saat ini. Fenomena konsumtif di era informasi ini sangat berpotensi dalam meningkatkan performa dari adanya potensi objek wisata di suatu wilayah. Berdasarkan latar belakang masalah tersebut melalui pemanfaatan teknologi informasi dan pelatihan teknis diharapkan mampu meningkatkan potensi Desa Sajen sebagai salah satu obyek pariwisata di Jawa Timur, Indonesia. Desa Sajen sendiri memiliki potensi dalam pariwisata yang didukung selain dari sumber daya alamnya, juga didukung dengan potensi-potensi kerajinan dari sumber daya manusia yang ada dan tinggal di Desa Sajen tersebut. Melalui pengabdian masyarakat ini, selain menunjang potensi pariwisata Desa Sajen melalui platform teknologi informasi, juga didukung dengan diadakannya pelatihan teknis mengenai pengelolaan teknologi informasi dan pemasarannya kepada warga Desa Sajen untuk dapat memaksimalkan potensi pariwisata Desa Sajen tersebut. Melalui Prodi D-3 Sistem Informasi mengembangkan sebuah portal online desa sebagai bentuk peningkatan exposure dari informasi dan potensi desa Sajen kepada masyarakat Indonesia.
Predicting tourist arrivals to a tropical island using artificial intelligence Suharto, Bambang; Edi Suharno, Novianto; Sinatriya Marjianto, Rachman; Firdaus, Aji Akbar; Suprapto, Sena Sukmananda; Andria Kusuma, Vicky; Amalia Sinulingga, Rizky
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1022-1030

Abstract

This research leverages artificial intelligence (AI) techniques to develop a predictive model for forecasting tourist arrivals in East Java Province, Indonesia, using a comprehensive dataset encompassing historical tourism statistics from 2018 to 2020, seasonal trends, promotional campaigns, and various economic and social variables. The study evaluates three AI methodologies: artificial neural network (ANN), extreme learning machine (ELM), and Jordan recurrent neural network (JRNN), each known for their distinct strengths in processing complex data and adapting to changing trends. The comparative analysis reveals that the JRNN model outperforms others with the highest precision, achieving an average prediction deviation of just 2.98% from actual data, effectively capturing temporal and seasonal trends. The ANN follows closely with a deviation of 3.31%, showing strong capabilities in handling complex, nonlinear relationships. In contrast, the ELM, though fastest in training, exhibits a larger deviation of 10.51%, indicating a trade-off between speed and accuracy. These results highlight the potential of AI to significantly enhance the accuracy and operational efficiency of tourism forecasts, offering robust tools for stakeholders to engage in informed strategic planning and resource allocation in dynamic market conditions.
COMPARISON OF XGBOOST AND RANDOM FOREST METHODS IN PREDICTING AIR POLLUTION LEVELS Pulih Asih, Akas Yekti; Yudianto, Firman; Triwinanto, Puguh; Sinatriya Marjianto, Rachman; Herlambang, Teguh; Arof, Hamzah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0785-0796

Abstract

Air is one of the elements needed by living things, including humans, to survive. The air quality in an area also affects the health and quality of human life and its surrounding environment. However, with the current phenomenon, the influence of the increasing number and mobility of humans actually degrades air quality, caused by the pollutants produced. For further impacts, poor air quality can reduce human life expectancy. Big cities in Indonesia, such as Surabaya, also experience the same thing due to the lack of public awareness of air pollution. The biggest contributors to air pollution are motor vehicles and industrial activities that emit carbon monoxide (CO), nitrogen oxides (NO), ozone (O3), and other particles (PM10). This condition is addressed by the Surabaya City Government by installing air condition measuring devices at points considered prone to pollution. This device works to measure urban air conditions daily and provides data that can be utilized to establish strategic policies. By utilizing the data, in this research, we implemented two prediction methods from machine learning technology, namely XG Boost and Random Forest. In accordance with the objective of this research, both methods will be compared for accuracy in predicting air pollution levels in Surabaya based on Ozon (O3) substance within the period of January 1, 2020, to December 31, 2020. Both of them have a similarity in that they implement tree-ensemble based, which are appropriate for handling non-linear data. The XG Boost method managed to achieve the best error value of 0.0510, and the Random Forest method reached the best error value of 0.0468.
Co-Authors Alifian Sukma, Alifian Andria Kusuma, Vicky Arof, Hamzah Bambang Suharto Firdaus, Aji Akbar Moh. Darus Salam Nanda Ridho Wirowicaksono Nasa Zata Dina Pulih Asih, Akas Yekti Sinulingga, Rizky Amalia Suharno, Novianto Edi Suprapto, Sena Sukmananda Taufik Bahtera, Novyandri Teguh Herlambang Tesa Eranti Putri Triwinanto, Puguh Yudianto, Firman