Article Per Year (5 Year)
p-Index From 2020 - 2025
4.293
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Wikrama Parahita : Jurnal Pengabdian Masyarakat Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Krea-TIF: Jurnal Teknik Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Generation Journal JATI (Jurnal Mahasiswa Teknik Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Pemberdayaan Komunitas MH Thamrin J-SAKTI (Jurnal Sains Komputer dan Informatika)
Enri, Ultach
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 29 Documents
Search

 1   2   3 
Workshop Pembuatan Mini Konveyor Untuk Proses Quality Control Berbasis Computer Vision Rozikin, Chaerur; Enri, Ultach; Suharso, Aries
Jurnal Pemberdayaan Komunitas MH Thamrin Vol 3, No 2 (2021): Jurnal Pemberdayaan Komunitas MH Thamrin
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jpkmht.v3i2.629

Abstract

Seiring berjalannya waktu, teknologi yang ada juga berkembang, perkembangan teknologi akan membantu manusia dalam kesehariannya. Otomatisasi adalah suatu teknologi yang terkait dengan mekanik, elektronik, dan komputer berdasarkan sistem untuk beroperasi dan untuk mengontrol produksi. Otomatisasi dapat digunakan dalam proses Quality Control dengan menggunakan Konveyor dengan Sensor hc05. Dengan sistem otomasi ini, proses Quality Control akan lebih cepat dan mengurangi tenaga kerja manusia di dalamnya. Cara kerjanya adalah dengan menyensor botol di dalam kotak untuk melihat jumlah botol dalam kotak sesuai dengan angka yang telah ditentukan.Kata Kunci: Kualitas Kontrol, Otomasi, Sistem Komputer Vision.
OPTIMASI PARAMETER SUPPORT VECTOR MACHINES UNTUK PREDIKSI NILAI TUKAR RUPIAH TERHADAP DOLLAR AMERIKA SERIKAT Enri, Ultach
Jurnal Gerbang STMIK Bani Saleh Vol 8 No 1 (2018): Informatics, Science and Technologies Journal
Publisher : LIPI

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

Abstract

Dengan perkiraan omzet rata-rata harian USD 5067 milliar pada April 2016 dan sebesar USD 5400 pada April 2013, bisa di lihat bahwa pasar valuta asing merupakan pasar terbesar dan paling aktif dari semua pasar keuangan, yang selalu bergerak dan tidak pernah statis. Oleh karena ketidakstabilan itu maka sangat penting untuk mengamankan investasi, managemen resiko dan juga kebijakan-kebijakan pengambil keputusan. Penelitian ini bertujuan untuk memprediksi nilai tukar mata uang dengan menggunakan optimasi parameter pada algoritma Support Vector Machines (SVM). SVM telah digunakan secara luas untuk peramalan keuangan yang mengunakan data set berupa timeseries serta menunjukan performa yang lebih baik dari pada algoritma lainnya.
Pengelompokkan Data Kemiskinan Provinsi Jawa Barat Menggunakan Algoritma K-Means dengan Silhouette Coefficient R, Nabila Nur Fransiska; Anggraeni, Dwi Suci; Enri, Ultach
TEMATIK Vol 9 No 1 (2022): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2022
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v9i1.901

Abstract

Serious poverty is still one of the problems in Indonesia, especially in West Java Province. The level of underdevelopment and unemployment is still the basis for poverty. Poverty in each region is certainly different. The government needs to know which areas fall into the categories of high poverty levels and low poverty levels so that they can make solutions to set priorities for assisting. Therefore, a data mining technique is needed that can classify the poverty level of areas in West Java, namely the clustering technique with the K-Means algorithm. The purpose of this research is to classify poverty data in West Java Province so that it can be used as information to determine the right policy to distribute aid to the community from the West Java government. The results obtained based on the test, the clusters obtained were 2 clusters with cluster 0 of the high poverty level in as many as 14 regions and cluster 1 of the low poverty level in as many as 13 regions. Based on the test, the K-Means Algorithm obtains a Silhouette Coefficient of 0.576 and is included in the medium structure category. With the results of grouping poverty data, the government can channel aid more precisely.
PENERAPAN ALGORITMA GAUSSIAN NAIVE BAYES DALAM PENENTUAN PRIORITAS REHABILITASI DAERAH ALIRAN SUNGAI BERDASARKAN PARAMETER LAHAN KRITIS Destiana, Tiara; Umaidah, Yuyun; Enri, Ultach
INFOTECH journal Vol. 9 No. 2 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i2.6501

Abstract

Berkurangnya sumber air, lapisan tanah yang subur mengalami erosi, longsor, dimana hal ini berdampak pada perubahan lahan kritis, yang menyebabkan penurunan kualitas Daerah Aliran Sungai (DAS). DAS Pemali Jratun memiliki lahan kritis seluas 559.492.530 hektar. Pendekatan klasifikasi dapat digunakan untuk mengidentifikasi wilayah yang memiliki lahan kritis dengan menggunakan algoritma Gaussian Naive Bayes selama proses data mining, dengan metodologi yang digunakan dalam penelitian ini adalah KDD. Skenario pembagian dataset terbagi menjadi 3 yaitu data 70:30, 80:20, dan 90:10, penelitian ini akan dibagi menjadi 5 klasifikasi, yaitu Sangat Kritis, Kritis, Agak Kritis, Kritis Potensial, dan Tidak Kritis. Hasil pengujian dari 3 skenario yang dibuat, pemodelan dari rasio 70:30, memiliki akurasi yang unggul. Nilai F1-Score 0,61, Precision 0,56, Recall 0,71, dan Accuracy 71%. Berdasarkan kesimpulan akhir klasifikasi, terdapat dua kelas lahan kritis penting yaitu kelas 1 dengan tingkat kekritisan lahan berpotensi kritis dan kelas 2 dengan tingkat kekritisan lahan agak kritis.
Perbandingan Algoritma SVM dan SVM Berbasis Particle Swarm Optimization Pada Klasifikasi Beras Mekongga Emilia Ayu Wijayanti; Rahmadanti, Tania; Enri, Ultach
Generation Journal Vol 5 No 2 (2021): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v5i2.16075

Abstract

Rice is the most important staple food in Indonesia. There are various types of varieties available, one of them is Inpari Mekongga variety. In Karawang, Mekongga rice type is the most popular and superior compared to others. However, this type of rice is often mixed with the other types because there are too many varieties and various other problems. Classifying varieties of rice types can be done to identify the types of rice. The classification of rice varieties in this research is divided into 2 classes, Mekongga and not Mekongga. The method that used in this reserach is Support Vector Machine (SVM) and Particle Swarm Optimatizon (PSO). SVM method was chosen because it basically handles the classification of two classes. Meanwhile, PSO method used to optimize the accuracy level of the SVM method. Combination from the two methods is very well used in classification data because it can increase the level of accuracy better. The purpose of this reserach is compare the accuracy of the 2 methods that used. The results from research is mekongga rice classification with Support Vector Machine has accuracy value 46.67% and AUC value 0.475. Meanwhile, using Support Vector Machine based on Particle Swarm Optimization (PSO) can help improve the classification of this mekongga rice with accuracy value 70.83% and AUC value 0.671.
Pengendalian Hama Tikus Sawah Berbasis Hayati dengan Burung Hantu Tyto Alba di Desa Sumberjaya, Kecamatan Tempuran, Kabupaten Karawang Afifah, Lutfi; Saputro, Nurcahyo W.; Adhi, Satriyo R.; Enri, Ultach
Wikrama Parahita : Jurnal Pengabdian Masyarakat Vol. 8 No. 2 (2024): November 2024
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jpmwp.v8i2.7467

Abstract

Tikus merupakan salah satu hama utama yang menyerang pertanaman padi di Desa Sumberjaya, Kecamatan Tempuran, Kabupaten Karawang. Pengen­dalian yang ramah lingkungan dapat menggunakan agens biologis burung hantu Tyto alba. Tujuan dari pengabdian masyarakat ini adalah untuk men­sosialisasikan dan memberi pelatihan kepada kelompok tani di Desa Sumber­jaya untuk bisa membuat dan melestarikan pengendalian berbasis hayati dengan menggunakan burung hantu untuk pengendalian tikus sawah. Kegiatan pengabdian masyarakat mitra nya yaitu kelompok tani Kedung Jaya, Desa Sumberjaya, Kecamatan Tempuran. Hasil dalam kegiatan peng­abdi­an masyarakat ini adalah 80% petani mampu membuat rubuha dan mampu melakukan konservasi musuh alami dengan penanaman refugia. T. alba datang secara alami dan mempergunakan sarang buatan tersebut sebagai tempat pemantauan tikus di areal persawahan. Rubuha yang dibuat oleh tim pengabdian masyarakat bersama dengan petani berjumlah satu rubuha, dengan adanya rubuha contoh tersebut petani mendapatkan pe­nge­tahuan untuk bisa membuat rubuha secara mandiri di Desa Sumberjaya. Adanya rubuha sebagai pengendali hama tikus dan penggunaan refugia untuk konservasi musuh alami menjadikan contoh bagi kelompok tani lain di Desa Sumberjaya untuk bisa mengendalikan hama secara ramah lingkungan.
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 Firmansyah, Faiz Agil Hannie, Hannie Hidayatulloh, Naufal Ammar Ilhamsyah, M. Iqbal Maulana Khairunisa, Nabila Lutfi Afifah, Lutfi Mayasari, Rini Mikhael Mulyadien, Muhamad Khandava Nugroho, Trio Nur Padilah, Tesa Nurpadilah, Tasya Pratama, Sena Purwantoro 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