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All Journal Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan IJCIT (Indonesian Journal on Computer and Information Technology) Wikrama Parahita : Jurnal Pengabdian Masyarakat Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENDIDIKAN TAMBUSAI Krea-TIF: Jurnal Teknik Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY JATI (Jurnal Mahasiswa Teknik Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Pemberdayaan Komunitas MH Thamrin Jurnal Ilmiah Wahana Pendidikan J-SAKTI (Jurnal Sains Komputer dan Informatika) Innovative: Journal Of Social Science Research VISA: Journal of Vision and Ideas
Enri, Ultach
Unknown Affiliation
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal Pilar Nusa Mandiri

 1 
ANALYSIS OF KARAWANG ONLINE SALES CUSTOMER SATISFACTION USING CUSTOMER SATISFACTION INDEX (CSI) METHOD Hannie, Hannie; Enri, Ultach; Umaidah, Yuyun
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1038.308 KB) | DOI: 10.33480/pilar.v16i1.1111

Abstract

Karawang is one of the industrial cities. Most industry players look at Karawang as a strategic city to run a business. Many products have been produced from Karawang. However, there are lack in promoting, marketing the product and expanding the marketing area. The analysis of consumer satisfaction in Karawang is to determine the satisfaction of Karawang consumers to the prospects of promising online sales. Service attributes can be included in increasing online sales at Karawang using the Customer Satisfaction Index (CSI) method. The result of the Customer Satisfaction Index (CSI) is 78.43% which means that overall consumers who live in Karawang and have been shopped online are satisfied with the development of online shopping. This research was conducted in Karawang. The data used are primary data and secondary data. The sampling method is a non-probability sampling method, while the non-probability sampling method used sampling purposes.
GOVERNMENT POLICIES MODELING IN CONTROLLING INDONESIA'S COVID-19 CASES USING DATA MINING Enri, Ultach; Sari, Eka Puspita
Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i1.2206

Abstract

Since the positive case of covid-19 in Indonesia, the government has taken several policies with the purpose of controlling the spread of the covid-19 virus, which has been regulated in Government Regulation No. 21 of 2020. The purpose of research is to obtain a model of government policy in controlling cases of covid by using data mining classification techniques, and obtain attributes that have the greatest weight, as well as look at the impact of policies that have been carried out by the government on the cases of covid-19 in Indonesia. The methodology used in the research is Knowledge Discovery In Database (KDD). Based on the research that has been done, it can be concluded that the policies that have been done by the government in controlling cases of covid-19 can be said to be successful, the C4.5 algorithm is the algorithm that gives the best results compared to the Deep Learning algorithm, as well as the attribute that has the greatest weight is cancel public events. Secondary data will be used in this research.
PREDICTION OF PUBLIC SERVICE SATISFACTION USING C4.5 AND NAÏVE BAYES ALGORITHM Umaidah, Yuyun; Enri, Ultach
Jurnal Pilar Nusa Mandiri Vol 17 No 2 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i2.2403

Abstract

One of the things that has often been questioned lately is in the field of public services, especially in terms of the quality or service quality of government agencies to the community, the Manpower and Transmigration Office of Kab. Karawang is a government agency in charge of public services. where one of the tasks is to make an AK.1 card (yellow card), based on this problem the Manpower and Transmigration Office of Kab. Karawang Regency. Karawang seeks to improve service quality in order to satisfy consumers by distributing questionnaires to every consumer who is making an AK card.1. In this study, we will apply the C4.5 and Naïve Bayes algorithms to predict the satisfaction of public services with the nominal type of dataset used. The evaluation is done based on a comparison of the level of accuracy, precision, recall, and F-Measure using a confusion matrix. From the research that has been carried out, the Naïve Bayes algorithm with 70% training data distribution and 30% testing is able to provide better predictive results than the C4.5 algorithm as evidenced by the accuracy value = 96.89%, precision = 95.50%, recall = 95.00% and f-measure = 94.60%.
IDENTIFICATION OF BACTERIAL SPOT DISEASES ON PAPRIKA LEAVES USING CNN AND TRANSFER LEARNING Ilhamsyah, M.; Enri, Ultach
Jurnal Pilar Nusa Mandiri Vol 18 No 1 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i1.2755

Abstract

Paprika, often called bell peppers, is a plant with the Latin name Capsicum annuum var. gross. Paprika in Indonesia has a high selling value, so the opportunity for cultivating the paprika plant itself is enormous. However, the cultivation of this plant cannot be separated from the threat of disease that can affect the yield of paprika. Bacterial spot is one of them, and it is a disease that is very dangerous for paprika plants because the disease infects all parts of the plant. In this case, early detection is needed to carry out appropriate treatment to minimize the effects caused by bacterial spots. Detection of bacterial spots on paprika can be done by direct observation or conducting laboratory tests, but this requires people who have the appropriate knowledge and experience. Based on the above problems, the identification system can be an option in identifying bacterial spot disease in paprika. This research chose the Convolutional Neural Network (CNN) algorithm in the identification system. Because CNN is one of the algorithms that can receive output in the form of an image which is very suitable for the case of bacterial spots on peppers, this research dataset is divided into healthy leaves and leaves infected with bacterial spots. In this study, the implementation of CNN with transfer learning obtained results from a test accuracy of 90%, training accuracy 97% with a loss of 8.5%, validation accuracy of 97.5% with a loss of 6.9%.
Co-Authors Adhi, Satriyo R. Adi Kurniawan Andreas, Felix Anesha Lutia, Frise Anggraeni, Dwi Suci Ariana Salsabila, Nazwa Aries Suharso Aufarrizqi, Adrian Beryl Enrico Ritonga, Samuel Damayanti, Ismi Destiana, Tiara E Haodudin Nurkifli Eka Puspita Sari Emilia Ayu Wijayanti Febrianto, Rifaldi Firmansyah, Faiz Agil Hannie, Hannie Heri Kurniawan, Heri Hidayatulloh, Naufal Ammar Ilhamsyah, M. Iqbal Maulana Khairunisa, Nabila Komarudin, Oman Lutfi Afifah, Lutfi Mayasari, Rini Miftakhurahmat, Moh. Aulia Mikhael Mulyadien, Muhamad Khandava Nugroho, Trio Nur Padilah, Tesa Nurhidayati, Lidya Nuria, Dwinda Putri Nurpadilah, Tasya Pratama, Sena Pratiwi, Jane Indah Purwantoro Putri, Tia Adha Mariam R, Nabila Nur Fransiska Rahmadanti, Tania Rahmah, Nabila Aulia Rahmayadi, Andhika Putra Utama Rozikin, Chaerur Rupaidah, Luvi Nur Sa'diah, Halimatu Saputro, Nurcahyo W. Sulhan Abdillah, M. Rio Syarif, Muhammad Himi Umaidah, Yuyun Utami, Dwiarti Rahma Windyani, Nona Alya