Garuda - Garba Rujukan Digital

ANALISIS TIME SERIES DENGAN METODE ARIMA DAN APLIKASINYA Fathorrozi Ariyanto; Moh. Badri Tamam
Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Vol 1 No 2 (2020): Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Oktober 2020
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/jatim.v1i2.972

Model time series yang sangat terkenal adalah model Autoregressive Integrated Moving Average (ARIMA) yang dikembangkan oleh George E. P. Box dan Gwilym M. Jangkins. Model time series ARIMA menggunakan teknik-teknik korelasi. Identifikasi model bisa dilihat dari ACF (Autocorrelation Function) dan PACF (Partial Autocorrelation Function) suatu deret waktu. Tujuan model ARIMA dalam penelitian ini adalah untuk menemukan suatu model yang akurat yang mewakili pola masa lalu dan masa depan dari suatu data time series. Pada penelitian ini, Penulis akan menganalisis penurunan algoritma suatu metode peramalan yang disebut metode peramalan ARIMA Kemudian menerapkan metode tersebut pada data riil yaitu data produksi air di PDAM Pamekasan dengan bantuan komputer dan software SPSS, yang nantinya akan diterapkan di dalam memberikan informasi dan analisis yang akurat terhadap perusahaan PDAM Pamekasan.Dari hasil pembahasan diperoleh rumus ARIMA yang berbentuk: Profit=+Y+Z, kemudian dari hasil penerapan data riil yaitu pada data produksi air di PDAM Pamekasan diperoleh model ARIMA (1 0 0) (0 0 1) sebagai model terbaik. Dengan model :

Application of the Smith Waterman and Jukes Cantor Algorithm in the Arrangement of the SARS CoV-2 Virus Tony Yulianto; Mohamad Tafrikan; Rica Amalia; Emi Yunita; Moch. Haikal; Fathorrozi Ariyanto; Zuhrotul Hasanah
Journal of Natural Sciences and Mathematics Research Vol 8, No 1 (2022): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jnsmr.2022.8.1.10887

In early 2020, the world was shocked by an outbreak of a new pneumonia that started in Wuhan, Hubei Province, which then spread rapidly to more than 190 countries and territories. This outbreak was named coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The spread of this disease has had a wide social and economic impact. There is still a lot of controversy surrounding this disease, including in the aspects of diagnosis, treatment, and prevention. Therefore, a study was carried out on studies related to COVID-19 that have been widely published since the beginning of 2020 until the end of March 2020.So to overcome this problem, the Smith Waterman Jukes Cantor Algorithm was made to align Covid19 by taking the a pair of DNA and RNA sequencesto align protein sequences. From this alignment, the percentage of identical and mutations will be known. The identical percentage in the genetic code will prove that although the symptoms caused by the disease are almost the same, the protein sequences are not necessarily the same. Based on the simulation results of the distance between sequences that produce a phylogenetic tree using the jukes cantor method, it was obtained that 4 groups of 26 sequences were divided into groups, namely, group 1 consists of 16 sequences, group 2 consists of 6 sequences, group 3 consists of 2 sequences, group 4 consists of 2 sequences. Based on these groups, it turns out that the China Wuhan sequence (sequence MT291826) is located in group 1 and other countries that are almost similar to the sequence in China Wuhan, namely the country of Timoe Leste with the sequence MT641766 also located in group 1.©2022 JNSMR UIN Walisongo. All rights reserved.

Best Regional Selection Decision Support System for Shallots in Waru District, Pamekasan Regency Using Topsis Method Fathorrozi Ariyanto; Rofiuddin
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 5 No. 2 (2020): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

The location of an area greatly influences the agricultural sector, including the Waru Subdistrict, Pamekasan Regency, a geographical condition that incidentally consists of hilly plains or highlands. So it is very influential on food plants that can be developed in the area. The objective to be achieved in this final project is to make a Decision Support System application for the Best Shallot Producing Region in Waru Subdistrict, Pamekasan Regency Using the TOPSIS Method. From the results of the study it can be concluded that the application system that has been made is running well and quite User Friendly, from the five villages in the Waru sub-district which were used as experimental data, it can be concluded that the selection of the best village using the TOPSIS method recommended for shallot cultivation is Sanah Laok village. with the highest score is 0.6312.

Pemberdayaan Masyarakat Pesisir Melalui Tata Kelola Wisata Bahari Berbasis Digital di Desa Lembung, Kabupaten Pamekasan Darmawan, Aang Kisnu Darmawan; Hadi, Saiful; Muqaddas, Zaiful; Ferdiansyah, Doni; Ariyanto, Fathorrozi; Wahyurini, Endang Tri
JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Vol 9, No 1 (2025): Mei
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.jpp-iptek.2025.v9i1.6613

Pengabdian ini didorong oleh kebutuhan mendesak untuk meningkatkan tata kelola dan keberlanjutan wisata bahari di Desa Lembung yang menghadapi tantangan koordinasi, infrastruktur, SDM, promosi, dan partisipasi masyarakat sehingga menghambat potensi wisata dan kesejahteraan masyarakat. Mitra pengabdian menghadapi kendala dalam berbagai aspek, berdampak pada kerusakan lingkungan, penurunan kualitas destinasi, rendahnya pendapatan masyarakat, dan persaingan tidak sehat. Pengabdian ini menerapkan solusi komprehensif, termasuk peningkatan koordinasi, penguatan infrastruktur, peningkatan kualitas SDM, promosi intensif, dan pemberdayaan masyarakat. Tahapan meliputi pembentukan tim, sosialisasi, pengembangan aplikasi, FGD, workshop, pembangunan infrastruktur, perancangan strategi, dan evaluasi. Pengabdian berhasil membentuk forum koordinasi dengan 3 pertemuan rutin (kehadiran 80%), memasang PLTS 600 WP dan 10 CCTV, mengadakan 2 workshop (60 peserta), menghasilkan aplikasi e-PesesserTour (50 unduhan, 10 pemesanan/bulan), meningkatkan pengikut media sosial sebesar 40%, membentuk 2 kelompok sadar wisata, dan melaksanakan 4 kegiatan bersih-bersih pantai (50 peserta/kegiatan). Dampak positif mencakup peningkatan wisatawan sebesar 25%, peningkatan pendapatan pelaku usaha pariwisata sebesar 20%, penurunan sampah di pantai sebesar 30%, serta peningkatan kepuasan wisatawan dan masyarakat terhadap kebersihan dan pengelolaan wisata. Pengabdian ini berhasil meningkatkan tata kelola, memberdayakan masyarakat, dan mewujudkan potensi wisata bahari Desa Lembung secara optimal, memberikan kontribusi nyata bagi kesejahteraan masyarakat dan keberlanjutan lingkungan.

DIGITAL TRANSFORMATION OF TOURISM VILLAGES: IMPLEMENTATION OF MOBILE APPLICATIONS AND E-PESESSERTOUR WEBSITES FOR PROMOTION OF LEMBUNG MARINE TOURISM Darmawan, Aang; Hadi, Saiful; Muqaddas, Zaiful; Daryanto, Eko; Tri Wahyurini, Endang; Ariyanto, Fathorrozi; Ferdiansyah, Doni
International Journal of Engagement and Empowerment (IJE2) Vol. 5 No. 1 (2025): International Journal of Engagement and Empowerment
Publisher : Yayasan Education and Social Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53067/ije2.v5i1.193

The potential of marine tourism, especially mangrove ecotourism, in Lembung Village, Pamekasan, has not been optimally promoted due to the limitations of conventional methods and minimal use of digital channels. This condition hinders the visibility and attractiveness of destinations in the digital era. This community service aims to initiate a digital transformation of Lembung Village tourism promotion by developing and implementing an integrated platform (mobile application and website) e-PesesserTour. The implementation method is participatory, involving local partners (Pokdarwis, BUMDes) in needs analysis, design, development, and implementation of the platform. The primary focus of implementation is increasing partner capacity through workshops and intensive mentoring in digital marketing strategies, content creation, and management of the e-PesesserTour platform (https://e-lembung-pesessertour.com) as a promotional tool. The results show the success of launching a functional, informative digital promotion platform that displays the village's attractions, including 3 new tour packages that have been developed. There was a significant increase in the digital capacity of partners, as evidenced by 90% of participants stating an increase in understanding of digital technology and an average score of post-training knowledge/skills tests reaching 85%. During the program implementation period, positive impacts were also observed in the form of an increase in tourist visits of up to 40%, an increase in BUMDes income of up to 50%, and a level of tourist satisfaction reaching 85%, some of which can be attributed to the rise in the quality of digital promotion and services. It was concluded that implementing the e-PesesserTour platform, supported by strengthening HR capacity, played a significant role as a catalyst for the digital transformation of Lembung Village tourism promotion, increasing the visibility and competitiveness of the destination sustainably.

Pemodelan Strategi Pemasaran Digital untuk Wisata Bahari Berkelanjutan dengan Pendekatan SWOT-AHP dan Porter's Five Forces Model Fathorrozi Ariyanto; Doni Ferdiansyah; Aang Kisnu Darmawan
Jurnal ICT: Information Communication & Technology Vol. 25 No. 1 (2025): JICT-IKMI, July, 2025
Publisher : LPPM STMIK IKMI Cirebon

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

This study aims to develop a sustainable digital marketing strategy model for marine tourism in Indonesia. The SWOT-AHP approach is used to identify and prioritize internal and external factors affecting the sector. Porter's Five Forces and Diamond Model analyses are applied to understand the competitive landscape and competitive advantages of Indonesia. The results of the SWOT-AHP analysis indicate that internal strengths and untapped marine tourism potential are dominant factors. The recommended digital marketing strategies are educational and conservation content marketing (priority 0.555), social media community building (0.265), SEO/SEM (0.118), and influencer marketing (0.062). Porter's Five Forces analysis highlights the importance of differentiation, building strong customer relationships, transparency, collaboration, and innovation. The Diamond Model analysis emphasizes the importance of utilizing natural resources, improving the quality of human resources, developing knowledge and technology, strengthening infrastructure, and collaborating with stakeholders. This study makes a significant contribution by providing a comprehensive and sustainable digital marketing strategy model for marine tourism in Indonesia. This model integrates SWOT-AHP analysis, Porter's Five Forces, and the Diamond Model, and considers economic and environmental sustainability objectives. The findings can help stakeholders develop responsible marketing strategies and promote sustainable tourism in Indonesia, driving economic growth while preserving the beauty and biodiversity of the sea.

APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS (CNN) FOR HEPATITIS C VIRUS (HCV) DISEASE DETECTION Fathorrozi, Fathorrozi Ariyanto; Ariyanto, Fathorrozi; Maulana, Indra; Hamzah, Moh. Aminollah; Kisnu Darmawan, Aang
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10542

Hepatitis C is a disease that attacks the liver and can progress to more serious conditions, such as cirrhosis or liver cancer, if not diagnosed and treated properly. Conventional diagnostic methods for Hepatitis C often face challenges in terms of efficiency and accuracy, so an innovative AI-based approach is needed to improve early detection. In this study, we apply a 1D Convolutional Neural Network (CNN) to classify Hepatitis C patients, using a dataset from Kaggle consisting of 615 samples with various medical parameters. The dataset goes through a series of preprocessing stages, including data cleaning, normalization, and feature transformation, before being applied to a 1D CNN model. The model is trained using the Adam optimizer, with ReLU activation functions in the convolution layer and sigmoid in the output layer. Model performance is evaluated through accuracy, precision, recall, and F1-score metrics. The results show that the developed 1D CNN model achieves an accuracy of 75% in detecting Hepatitis C. Although these results show promising potential, there is still room for improvement through exploration of more complex architectures or the use of larger datasets. Thus, this research is expected to make artificial intelligence an effective tool in the diagnosis of Hepatitis C, increasing accuracy and efficiency in the process. Keywords: Hepatitis C, 1D CNN, Deep Learning, Disease Classification, Medical Diagnosis

APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS (CNN) FOR HEPATITIS C VIRUS (HCV) DISEASE DETECTION Fathorrozi, Fathorrozi Ariyanto; Ariyanto, Fathorrozi; Maulana, Indra; Hamzah, Moh. Aminollah; Kisnu Darmawan, Aang
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10542

Hepatitis C is a disease that attacks the liver and can progress to more serious conditions, such as cirrhosis or liver cancer, if not diagnosed and treated properly. Conventional diagnostic methods for Hepatitis C often face challenges in terms of efficiency and accuracy, so an innovative AI-based approach is needed to improve early detection. In this study, we apply a 1D Convolutional Neural Network (CNN) to classify Hepatitis C patients, using a dataset from Kaggle consisting of 615 samples with various medical parameters. The dataset goes through a series of preprocessing stages, including data cleaning, normalization, and feature transformation, before being applied to a 1D CNN model. The model is trained using the Adam optimizer, with ReLU activation functions in the convolution layer and sigmoid in the output layer. Model performance is evaluated through accuracy, precision, recall, and F1-score metrics. The results show that the developed 1D CNN model achieves an accuracy of 75% in detecting Hepatitis C. Although these results show promising potential, there is still room for improvement through exploration of more complex architectures or the use of larger datasets. Thus, this research is expected to make artificial intelligence an effective tool in the diagnosis of Hepatitis C, increasing accuracy and efficiency in the process. Keywords: Hepatitis C, 1D CNN, Deep Learning, Disease Classification, Medical Diagnosis

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