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All Journal Jurnal Ilmiah Informatika Komputer KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Indonesian Journal of Applied Informatics Jurnal ICT : Information Communication & Technology Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Jurnal Informatika Terpadu International Journal of Social Science Jurnal Dimamu AMMA : Jurnal Pengabdian Masyarakat Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal)
Nana Suarna
STMIK IKMI Cirebon
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analisa Clustering untuk Mengelompokan Data Penayangan Film Bioskop Menggunakan Algoritma K-Means Moh Nurdayat Dayat; Nana Suarna; Yudhistira Arie Wijaya
INTERNAL (Information System Journal) Vol. 6 No. 1 (2023)
Publisher : Masoem University

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

Abstract

The purpose of this study is one of the analyzes to obtain film screening data, the approach used in this study is the K-means algorithm using the parameter measure type Numerical Measure with Numerical Measure Euclidean Distance to get the best Davies Bouldin Index (DBI), with the intention of getting helps grouping datasets of film screenings at the Ramayana Cirebon XXI Cinema. Results from the evaluation of the Davies Bouldin Index (DBI) obtained is (K-2) with a Davies Bouldin Index (DBI) value of 0.864, because the value obtained is the smaller the Davies Bouldin Index (DBI) value, it shows the optimum performance of the resulting cluster.
Usability Testing pada Aplikasi Kas Berbasis Android dan Teknologi API menggunakan Metode System Usability Scale Nurul Ibnu Al Muharom; Nana Suarna; Raditya Danar Dana
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.v10i1.1099

Abstract

Cash management applications are used to store transaction data and create cash in and cash out reports that are more effective than using books. However, currently no usability testing has been carried out so it is quite difficult to determine future application development steps. The aim of this research is to measure the level of usefulness of an Android-based cash management application using the System Usability Scale (SUS) method, namely a 10-question Likert scale questionnaire distributed to respondents with the final result being a score of 0-100. The number of respondents was 15 people who were CV employees. Jaya Mukti. This test was carried out to determine the level of user convenience and satisfaction. The results of the research obtained a score of 84.7, this score shows a percentile level of 96%, Grade Scale A, Acceptability Ranges are in the Acceptable category, meaning acceptable, and the Adejctive Rating is in the Excellent category. If correlated with NPS, it falls into the promoter category, meaning users will recommend the application. These results show that the application can be accepted by users and is very good and above the established usability value standards. This research also produced 4 recommendations for improvement based on the results of questionnaire answers so that they can be used as evaluation for future application development.
Prediksi Jumlah Sampah pada Sektor Informal di Provinsi Jawa Barat MenggunakanAlgoritma Regresi Linear Nursyifa Puspa Ar-rahmi Slamet; Nana Suarna; Willy Prihartono
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10294

Abstract

Waste has become one of the most pressing global problems to be solved. Rapid population growth, urbanization, and consumerism have led to a significant increase in the volume of waste worldwide. This phenomenon not only affects the environment, but also touches the economic sector, health, and social life. West Java, as one of the provinces with the highest population density in Indonesia, faces great pressure regarding waste management. The province is experiencing a significant increase in the amount of waste that occurs due to population growth and high intensity of industrial activities. The method used in this research is linear regression algorithm. The application of linear regression algorithm can help the government to plan strategic measures in waste management. By using historical data on waste production, population growth, and other factors. This algorithm can provide an overview of future trends in waste generation. The purpose of this research is to implement a linear regression algorithm to predict the amount of waste data that goes to the informal sector, especially involving collectors or stalls in West Java province. The results of this study resulted in an increase in the accuracy level of the accuracy of the volume of waste in the informal sector in West Java Province can have a significant impact and make a major contribution to the understanding of the effectiveness of the application of linear regression algorithms. This increase in accuracy is expected to deepen the understanding of how the algorithm can be optimized for more efficient prediction and management of waste volume.
PENERAPAN ALGORITMA SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN ULASAN PELANGGAN TOKO LIVIA CIREBON DI SHOPPE Syaeful Annas; Nana Suarna; Irfan Ali; Heliyanti Susana
Jurnal Ilmiah Informatika Komputer Vol 29, No 3 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i3.13109

Abstract

Analisis sentimen adalah proses yang bertujuan untuk memahami opini pelanggan dengan mengklasifikasikan ulasan menjadi sentimen positif, netral, atau negatif. Penelitian ini bertujuan untuk mengembangkan model analisis sentimen berbasis algoritma Support Vector Machine (SVM) terhadap ulasan pelanggan Toko Livia Cirebon di platform Shopee. Pendekatan penelitian dilakukan secara kuantitatif, dengan tahapan meliputi pengumpulan data, pra-pemrosesan teks (cleansing, normalisasi slang, tokenisasi, penghapusan stopword, dan stemming), pelabelan menggunakan Inset Lexicon, transformasi data teks menjadi vektor numerik dengan metode TF-IDF, pelatihan model SVM, serta evaluasi performa menggunakan metrik akurasi, precision, recall, dan F1-score. Model yang dikembangkan mencapai akurasi sebesar 91% dengan performa terbaik pada sentimen positif (F1-score 95%), meskipun performa pada kategori netral dan negatif masih memerlukan peningkatan. Hasil penelitian ini menunjukkan bahwa algoritma SVM efektif untuk analisis sentimen dalam e-commerce, memberikan wawasan strategis bagi pemilik usaha untuk menyusun strategi pemasaran dan meningkatkan kualitas layanan.
Pengelompokan Regu Penyelamat Non-Kebakaran di Kabupaten Cirebon dengan K-Means Clustering Arye Fandia Kusuma; Nana Suarna; Irfan Ali; Dodi Solihudin
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.96225

Abstract

Abstrak : Layanan darurat non kebakaran di Kabupaten Cirebon menghadapi beberapa kendala, seperti alokasi sumber daya yang tidak efisien dan terbatasnya pemanfaatan analisis data. Penelitian ini bertujuan untuk meningkatkan pengelolaan regu penyelamat dengan memanfaatkan algoritma K-Means sebagai metode pengelompokan data. Menggunakan pendekatan data mining dan penerapan Knowledge Discovery in Database (KDD), penelitian ini menganalisis 874 data layanan darurat non kebakaran yang dikumpulkan pada tahun 2023-2024. Data yang dianalisis meliputi Lokasi kejadian, jenis penyelamatan, tingkat keparahan insiden, waktu respons, dan distribusi regu penyelamat. Proses penelitian dimulai dengan konversi data kedalam format numerik. Hasil menunjukan nilai DBI terendah sebesar 0,080 dengan empat klaster optimal, yang meningkatkan efisiensi distribusi regu penyelamat=====================================================Abstract : Non-fire emergency services in Cirebon Regency face several obstacles, such as inefficient resource allocation and limited utilization of data analysis. This study aims to improve the management of rescue squads by utilizing the K-Means algorithm as a data clustering method. Using a data mining approach and the application of Knowledge Discovery in Database (KDD), this study analyzed 874 non-fire emergency service data collected in 2023-2024. The data analyzed included Location of incident, type of rescue, severity of incident, response time, and distribution of rescue squads. The research process started with converting the data into numerical format. The results showed the lowest DBI value of 0.080 with four optimal clusters, which improved the efficiency of rescue squad distribution.
Co-Authors Abdul Ajiz Abdul Rasyid Faiq Hadinata Achmad Baihaqi Ade Irma Purnamasari Akhyanto Akhyanto Arye Fandia Kusuma Basysyar, Fadhil Muhammad Cep Lukman Rohmat Danya Rizki Chaerunisa Dion Nugraha Dita Amalia Rizki Dita Rizki Amalia Dodi Solahudin Dodi Solihudin Dwi Atmoko Elisa Sriyulia Ervina Mahara Fadhil Muhammad Basysyar Fadrin Helmi Fathurrohman Febriyana Lorenza Heliyanti Susana Herman Iin Irfan Ali Kuju Susilawati Lin Lin Martanto . Melisa Hikari Moh Nurdayat Dayat Mulyawan Mulyawan Nana Mulyanasari Nanda Septian Nining Rahaningsih Nur Kholidin Nursyifa Puspa Ar-rahmi Slamet Nurul Ibnu Al Muharom Odi Nurdiawan Opiana . Raditya Danar Dana Rini Astuti Rini Astuti Ryan Hamonangan Saeful Anwar Sayudin Sayudin Shandy Alfian Sri Muflikah Kurniarti Sulaeman Syaeful Annas Usup Supendi Willy Prihartono Yudhistira Arie Wijaya