Garuda - Garba Rujukan Digital

Implementasi Algoritma K-Means Dalam Pengelompokan Jumlah Wisatawan Akomodasi Di Jawa Barat Neni Lusianah; Ade Irma Purnamasari; Bani Nurhakim
Jurnal Ekonomi, Bisnis dan Manajemen Vol. 2 No. 1 (2023): Maret: Jurnal Ekonomi, Bisnis dan Manajemen
Publisher : FEB Universitas Maritim Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/ebismen.v2i1.682

Akomodasi merupakan orientasi sosial yang bermakna keutuhan sosial demi menjauhi serta mendamaikan kegentingan, pertikaian yang saling berkaitan. Seumpama bentuk kegiatan akomodasi yang bermanfaat mempersiapkan perlengkapan guna melengkapi keperluan. provinsi jawa barat terdapat tempat wisata yang ramai dikunjungi oleh wisatawan nusantara dan mancanegara oleh sebab itu pengunjung menggunakan fasilitas akomodasi yang tersedia di setiap daerah, wisatawan nusantara ramai menginap di villa sedangkan wisatawan mancanegara lebih memilih hotel berbintang. Penelitian ini memfokuskan bagaimana pengelompokan wisatawan daerah wisata akomodasi dan jenis wisatawan terbanyak yang ramai berkunjung menggunakan algoritma k-means, analisis ini memakai metode Knowledge Discovery in Database (KDD). Teknik pengumpulan data atau pengumpulan data bersumber dari Dinas Pariwisata dan Kebudayaan Provinsi Jawa Barat. Dengan tujuan kiranya menjadi pengetahuan untuk wisatawan serta mampu mempublikasikan wisata di media sosial dan diharapkan akan lebih meningkat jumlah wisatawan yang berkunjung dan meningkatnya pembangunan sarana dan prasarana di setiap daerah akomodas.Hasil dari penelitian ini bahwa daerah akomodasi yang ramai dikunjungi adalah Kota Bandung dengan jenis wisatawan yang datang yaitu pengunjung dalam negeri (Nusantara).

Penerapan Pengelompokkan Produktivitas Hasil Pertanian Menggunakan Algoritma K-Means Putri Trisnawati; Ade Irma Purnamasari
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.10198

Since ancient times, Indonesia has always been rich in agricultural products such as rice, soybeans, corn, peanuts, cassava, and sweet potatoes. In addition, there are also products from agriculture that are referred to as trade crop agricultural products, namely tea, coffee, coconut, quinine, cloves, sugar cane, rubber, and others. The agricultural sector in 2021 will grow by 1.84% and contribute 13.28% to the national economy. Then in 2022, the agricultural sector will show consistency with a positive growth of 1.37% and contribute 12.98% to the national economy. Then it is necessary to group the productivity of agricultural products using the k-means clustering method to group data on the highest and lowest yield types according to the District in Bojonegoro so that the types of agricultural products that are most productive and less productive can be identified. The method used in this study is K-Means cluster analysis by first determining the number of groups to be formed. In this study, the data used is secondary data on agricultural products originating from One Bojonegoro Data. The food crops in question are rice, shallots, soybeans, large chilies, corn, and so on. From the results of grouping agricultural products based on the year of production, the best types of crops will be known, and which districts will produce the most productive food crops so that the distribution of food crops in Bojonegoro District can be controlled. Productivity grouping of agricultural products can be used as a strategy to increase agricultural yields.

PKM E-Commerce Pengrajin UMKM Batik Desa Trusmi Odi Nurdiawan; Ahmad Faqih; Ade Irma Purnamasari; Salsa Billa Agistina; Nanda Permatasari
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 10 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

The development of information technology has also changed the views of information technology activists to create breakthroughs in the business sector and promotions to increase competitiveness in developing business. Breakthroughs in the business world, especially the buying and selling business, have led to the use of mobile phones. The use of mobile phones has changed its function to become a multifunctional device for running various kinds of mobile applications, both media for accessing and processing information. Marketplace is an electronic market (virtual market) where the market is a meeting place for buyers and sellers to carry out transactions. Marketplaces are in principle and function the same as traditional markets, only if this marketplace is computerized and connected to the internet. Marketplace or electronic market that brings together sellers and buyers who are connected through electronic transactions that can be accessed quickly, safely, and can be done anywhere and at any time [1] [5] Problems in Community Service The marketing and sales system in craftsmen is still manually, namely the batik craftsman gives the results to the collector (showroom), the showroom or shop owner will resell it at a high price, for example the batik craftsman working on written batik cloth with a Mega Mendung motif is priced at IDR 250,000 by the showroom, then the showroom owner resells the batik cloth at a price a fantastic Rp. 500,000. The urgency of Community Service is how to shorten the distribution of sales of batik craftsmen's products using dodolan application technology. The purpose of the "Dodolan" application is a place for marketing and selling products made by batik craftsmen in Trusmi village, Cirebon district, which is made in the form of an Android-based application. The presence of the Dodolan Application is an effort to realize the attention of batik craftsmen in terms of marketing and sales. In achieving the goal of Community Service, we use the Agile Software Development Cycle with the Extreme Programming (XP) method. The Community Service Stage consists of 5 stages, namely: 1. Meeting Stage, namely brainstorming with users about plans to create a system; 2. Planning stage, namely making Iterations Schedule and Milestones; 3. Design stage, namely making UX-UI Design; 4. Development and Testing stage carried out by the programmer; and 5. Evaluation stage which consists of testing by users, patching by programmers, and project documentation.

Pemanfaatan Sistem Informasi Layanan Berbasis Digital Desa Sinarancang Odi Nurdiawan; Ahmad Faqih; Ade Irma Purnamasari; Adisty Tri Putra; Kurniawan Fajar Abdulloh
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 8 : September (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Utilization of Digital-Based Service Information Systems has become a major concern in efforts to improve service quality and progress in various rural areas. Sinarancang Village, as an example of a village facing various challenges in terms of access to information and services, has taken bold steps to adopt a digital-based information system as the main tool to overcome this problem. This Community Service explains how Sinarancang Village has integrated information technology into their services with the aim of increasing efficiency and accessibility. Community Service is an initiative that has been taken by the village government, community, and local partners in developing an inclusive information technology infrastructure. In addition, the success of Community Service has been achieved through the use of digital-based information systems in increasing access to health, education and village administration services. By combining a participatory approach and community education, Sinarancang Village has succeeded in creating an environment that supports the adaptation of information technology at the community level. The results of implementing this digital-based service information system show great potential for improving the quality of life of rural communities, promoting inclusivity, and encouraging economic growth in rural areas, one of which was initiated by the Anti Galau Sinarancang tourism village.

Perancangan Website Pembelajaran Bahasa Jepang dengan Fokus Materi Klausa melalui Metode Waterfall Dimas Ardiansyah; Ade Irma Purnamasari
Jurnal Informatika Terpadu Vol 10 No 1 (2024): Maret, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i1.1085

Interest in Japanese is increasing in Indonesia, especially at LPK IHMI Cirebon, along with the growth of bilateral relations and information technology. This research aims to improve Japanese language learning, especially Japanese clause material through website design at LPK IHMI Cirebon. Conventional methods that are less interactive become obstacles to active participation of students. An innovative approach is proposed by utilizing a website, especially through the WordPress platform, and referring to the book "Minna no Nihongo" as a source of comprehensive learning material. The choice of WordPress platform is based on ease of use and integration with other services. LPK IHMI Cirebon faces the challenge of learning resources and conventional methods that are less interactive, impacting student participation, progress, motivation and understanding. The Waterfall research method was used with a focus on developing web learning modules, especially in chapter 22 "Minna no Nihongo" regarding Japanese clause material which is still considered the most difficult material in Japanese. Implementation involves the integration of learning technologies, especially Artificial Intelligence, to increase interactivity. The test results show that the website works as planned with an appropriate effectiveness level of 100% and provides positive performance as a Japanese language learning tool of 95%.

ANALYSIS STUDENT EMOTIONS AND MENTAL HEALTH ON CUMULATIVE GPA USING MACHINE LEARNING AND SMOTE Fadhil Muhammad Basysyar; Gifthera Dwilestari; Ade Irma Purnamasari
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5967

This research investigates the impact of emotions and mental health on students' cumulative grade point average (CGPA) using machine learning classification algorithms while addressing data imbalances with the Synthetic Minority Oversampling Technique (SMOTE). Emotional well-being and mental health are acknowledged as vital determinants of academic achievement. Data imbalance, particularly in mental health metrics such as anxiety and depression, frequently compromises forecast accuracy. This study improves the accuracy of CGPA prediction based on emotional and mental health factors by utilizing SMOTE in machine learning models such as logistic regression and random forest. A dataset including 226 university students, including academic records and self-reported mental health evaluations, was evaluated. The random forest model attained an accuracy of 87.63%, exceeding the logistic regression model's accuracy of 86.56%. These findings emphasize the significant role of emotions and mental health in academic outcomes and validate SMOTE’s efficacy in addressing class imbalance. This work offers a fresh technique in educational data mining by revealing the possibility for improved academic achievement forecasts based on psychological characteristics, helping to the development of targeted therapies for students experiencing emotional issues. Implications for educational policy emphasize the significance of mental health support systems in promoting academic achievement. Subsequent research should investigate supplementary psychological variables and comprehensible models to improve predictive accuracy and facilitate evidence-based policymaking.

Peningkatan Kompetensi Siswa SMK Melalui Pelatihan Junior Web Developer Dalam Pengembangan Website Ade Irma Purnamasari; Yudhistira Arie Wijaya; Aditiya Arif Firmansyah; Intan Wangi Nur Qibti
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 2 : Maret (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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This community service program aims to enhance the competencies of vocational high school (SMK) students in web development through a Junior Web Developer training program. The activities were carried out at several SMKs in Cirebon Regency and Cirebon City as an effort to bridge the gap between school curricula and industry needs. The implementation methods included preparation and planning, training execution, monitoring and evaluation, as well as dissemination of results. The outcomes of this program showed a significant improvement in students' understanding and skills in web development technologies, particularly in the use of HTML, CSS, JavaScript, and modern frameworks. In addition, participating teachers also benefited from workshops designed to enhance their ability to teach industry-based materials. This program successfully produced several outputs such as learning modules, student web projects, and collaborations with industry partners to open up job opportunities for graduates. With this program, it is expected that vocational students will have better competencies to face the workforce and the digital industry. The sustainability of this program can be expanded by increasing industrial partnerships and broadening the scope of training participants.

Analisa Penggunaan Metode Lexicon Based Dan Algoritma Naive Bayes Pada Sentimen Ulasan Aplikasi Duolingo Muhammad Abib Allesdio; Ade Irma Purnamasari; Irfan Ali; Nana Suarna; Agus Bahtiar
Jurnal Sistem Informasi dan Teknologi Vol 6 No 2 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i2.261

Peningkatan jumlah ulasan pengguna pada aplikasi mobile membuka peluang untuk memahami persepsi dan pengalaman pengguna melalui analisis sentimen. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Duolingo yang diambil dari Google Play Store menggunakan dua pendekatan, yaitu metode lexicon-based dan algoritma Naive Bayes berbasis Python. Metode lexicon-based digunakan untuk memberikan skor polaritas berdasarkan leksikon sentimen, sedangkan Naïve Bayes diterapkan sebagai model klasifikasi dengan dukungan fitur TF-IDF. Proses penelitian meliputi tahapan pengumpulan data, preprocessing teks (cleaning, case folding, tokenisasi, stopword removal, dan stemming), pembobotan sentimen, pelatihan model, serta evaluasi performa menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa metode lexicon-based mampu memberikan gambaran umum polaritas ulasan, namun performanya sangat dipengaruhi oleh kelengkapan leksikon dan variasi bahasa informal pengguna. Sementara itu, algoritma Naive Bayes menunjukkan performa yang lebih stabil dan akurasi lebih tinggi dalam mengklasifikasikan sentimen dibandingkan pendekatan leksikon. Perbandingan kedua metode memperlihatkan bahwa Naive Bayes lebih efektif dalam menangani data teks pendek, tidak terstruktur, serta mengakomodasi variasi kata dan ejaan. Temuan penelitian ini memberikan pemahaman yang lebih dalam mengenai persepsi pengguna terhadap Duolingo serta menjadi referensi metodologis bagi penelitian sentiment analysis selanjutnya, khususnya yang melibatkan kombinasi metode leksikon dan klasifikasi probabilistik.

Optimalisasi Convolutional Neural Network Kontra VGG16 Klasifikasi Citra Daun Sawi Rio Febriyan; Ade irma Purnamasari; Denni Pratama; Puji Pramudya Marta; Yudhistira Arie Wijaya
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 6 No 1 (2026): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol6No1.pp30-34

Manual detection of pests on mustard greens (caisim) is a major constraint in reducing harvest productivity, as manual methods are inefficient, time-consuming, and require specialized expertise. Furthermore, deep learning models often suffer from overfitting when applied to limited agricultural datasets. This study aimed to develop and compare the effectiveness of a Convolutional Neural Network (CNN) from scratch model versus the VGG16 transfer learning architecture for automatic classification of healthy and pest-affected mustard leaf images. A dataset of 1,000 images was used for training and testing across four experimental scenarios (A to D), with Percobaan C being the optimized CNN from scratch model (using data augmentation) and Percobaan D using VGG16. The results showed that the VGG16 transfer learning model achieved the highest test accuracy of 95.0% (F1-score: 0.95), while the optimized CNN from scratch model achieved 92.0% (F1-score: 0.92). Therefore, transfer learning with VGG16 is the most effective and optimal approach, demonstrating superior performance and efficiency by achieving high accuracy without complex data augmentation.

Analisa Penggunaan Metode Lexicon Based Dan Algoritma Naive Bayes Pada Sentimen Ulasan Aplikasi Duolingo Muhammad Abib Allesdio; Ade Irma Purnamasari; Irfan Ali; Nana Suarna; Agus Bahtiar
Jurnal Sistem Informasi dan Teknologi Vol 6 No 2 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i2.261

Peningkatan jumlah ulasan pengguna pada aplikasi mobile membuka peluang untuk memahami persepsi dan pengalaman pengguna melalui analisis sentimen. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Duolingo yang diambil dari Google Play Store menggunakan dua pendekatan, yaitu metode lexicon-based dan algoritma Naive Bayes berbasis Python. Metode lexicon-based digunakan untuk memberikan skor polaritas berdasarkan leksikon sentimen, sedangkan Naïve Bayes diterapkan sebagai model klasifikasi dengan dukungan fitur TF-IDF. Proses penelitian meliputi tahapan pengumpulan data, preprocessing teks (cleaning, case folding, tokenisasi, stopword removal, dan stemming), pembobotan sentimen, pelatihan model, serta evaluasi performa menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa metode lexicon-based mampu memberikan gambaran umum polaritas ulasan, namun performanya sangat dipengaruhi oleh kelengkapan leksikon dan variasi bahasa informal pengguna. Sementara itu, algoritma Naive Bayes menunjukkan performa yang lebih stabil dan akurasi lebih tinggi dalam mengklasifikasikan sentimen dibandingkan pendekatan leksikon. Perbandingan kedua metode memperlihatkan bahwa Naive Bayes lebih efektif dalam menangani data teks pendek, tidak terstruktur, serta mengakomodasi variasi kata dan ejaan. Temuan penelitian ini memberikan pemahaman yang lebih dalam mengenai persepsi pengguna terhadap Duolingo serta menjadi referensi metodologis bagi penelitian sentiment analysis selanjutnya, khususnya yang melibatkan kombinasi metode leksikon dan klasifikasi probabilistik.

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