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All Journal Jurnal technoscientia Jurnal Informatika dan Teknik Elektro Terapan Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics Information Management For Educators And Professionals (IMBI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURNAL ILMIAH INFORMATIKA JURIKOM (Jurnal Riset Komputer) Jurnal Informasi dan Komputer JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal Tekno Kompak Jurnal ICT : Information Communication & Technology Jurnal Manajemen Komunikasi Jurnal Informatika dan Rekayasa Perangkat Lunak JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) E-Link: Jurnal Teknik Elektro dan Informatika Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) MEANS (Media Informasi Analisa dan Sistem) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Informatika Terpadu Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika dan Teknologi Informasi INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi JURSIMA Jurnal Teknologi Ilmu Komputer AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Sistem Informasi dan Manajemen Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Informasi interaktif : jurnal informatika dan teknologi informasi
Gifthera Dwilestari
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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Peningkatan Daya Saing UMKM Desa Lemah Abang Melalui Pembangunan Dan Manajemen Website Gifthera Dwilestari; Edi Tohidi; Raena Agustin Laeliyah; Anita Nur Kirana; Hilya Ashfia Nabila
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 09 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

This community service is an initiative that aims to overcome the challenges faced by Micro, Small and Medium Enterprises (MSMEs) in Lemahabang Village in facing the digital era[1]. In the midst of the rapid development of information technology, websites have become a crucial tool for MSMEs in developing their business. However, many MSMEs in rural areas such as Lemahabang Village still face obstacles in understanding and managing their websites. In this situation analysis, we will explain the location of the partners and the cases that occurred, the social and cultural conditions of Lemahabang Village, the special problems faced by MSMEs there, the methods or approaches used in the service, and the implications of the results of the service for the community. The results of the activity "Increasing the Competitiveness of MSMEs in Lemah Abang Village through Website Development and Management" have had a very positive impact on MSMEs in the village. The implementation of the website has succeeded in expanding the market reach of MSMEs in Lemah Abang Village. There was a significant increase in the number of visits to the website, which indicates greater interest from consumers outside the region and there was a marked increase in the sales volume of MSME products after the implementation of the website. This reflects the effectiveness of digital platforms in supporting business transactions.
IMPLEMENTASI PADA PENGELOMPOKKAN DATA STUNTING BALITA MENGGUNAKAN ALGORITMA CLUSTERING K-MEDOIDS Puspita Maulana Arumsari; Umi Hayati; Gifthera Dwilestari
JURNAL ILMIAH BETRIK Vol. 14 No. 01 APRIL (2023): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v14i01 APRIL.23

Abstract

The state of chronic malnutrition in toddlers as measured by the World Health Organization (WHO) standard in 2005, based on height for age, which is a combination of short and very short terms with a Z-score <- 2 standard deviations. The results of the Indonesian Nutrition Status Study (SSGI) of the Ministry of Health show that 24.5% of infants under 5 years old (toddlers) in West Java will experience stunting in 2021. Nearly a quarter of toddlers whose height is below the standard for their age. The data used in this study were obtained from the Ministry of Home Affairs' Bangda Action by monitoring the implementation of 8 convergence actions for stunting reduction interventions. The direction of this research refers to grouping data on the distribution of the number of stunting in the Regency and City of Cirebon based on data collection results in 2022. In this study, the data were processed by applying the K-Medoids clustering algorithm or called Partitioning Around Medoids (PAM) by using the partition clustering method for grouping a set of n objects into several k clusters. From the results of grouping with 3 clusters, the DBI value was -2.427, wherein in the high-level stunting cluster there were 102 villages. In contrast, the medium-level percentage cluster was 103 villages, and the low-level stunting percentage cluster was 241 villages. This research is expected to provide information to the government regarding the classification of stunting under five so that it can determine which villages still need treatment in reducing stunting.
ANALISIS ALGORITMA KLASIFIKASI NEURAL NETWORK PADA PENDERITA PENYAKIT KANKER PAYUDARA Auliya; Tati Suprapti; Gifthera Dwilestari
JURNAL ILMIAH BETRIK Vol. 14 No. 01 APRIL (2023): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v14i01 APRIL.24

Abstract

Breast cancer is classified as a type of malignant disease and ranks first in terms of the highest number of cancers in Indonesia and is one of the first contributors to deaths from cancer. About 43% of deaths from cancer can be prevented if breast cancer sufferers routinely carry out early detection or early diagnosis and avoid risk factors that cause cancer. In this study, a classification data mining technique will be used to predict living and deceased status using the Neural Network algorithm with rapidminer 10.0 tools. Neural network algorithm is a neural network of the human brain that is designed to follow the way the human brain processes and stores information in carrying out pattern recognition tasks, especially classification. The results of the accuracy show that the ratio of correct predictions with all data is 89.22%. With a true positive class recall of 97.08%, a true negative class recall of 49.12%, a precision Pred class. positive by 90.69% and Class precision Pred. negative by 76.71%. Analysis of positive breast cancer patients died as many as 565 records. With this classification benchmark, it is hoped that it can reduce mortality from breast cancer.
Implementasi Algoritma Regresi Linear Berganda untuk Memprediksi Biaya Asuransi Kesehatan Bagas Al Haddad; Agus Bahtiar; Gifthera Dwilestari
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.v6i1.10262

Abstract

Technological developments such as telemedicine and big data analysis have had a significant impact on the health insurance industry. It is very difficult to make wise decisions if customers do not understand the cost of insurance. Age, gender, medical history, region, smoking, and body mass index (BMI) are a number of variables used to determine the variables that contribute to health insurance costs. Multiple linear regression was used to identify variables that contribute to predicting relative health insurance costs. Multiple linear regression analysis, also known as multiple regression analysis, is a regression model that involves more than one independent variable. This is determined by using statistical software to determine which independent variables have a significant influence on the dependent variable. The value of using multiple linear regression is primarily related to the need for prediction of insurance costs. In the RapidMiner tool, the linear regression operator is used to perform linear regression calculations. From a total of 1338 datasets, the data is divided into two parts. 90% is used as training data (with a total of 1204 data) and 10% is used as test data (with a total of 134 data). The results of the analysis show that independent factors such as smoking status, age, and body mass index have a significant correlation with insurance premium costs. The value 5891.019 was generated from model evaluation using Root Mean Squared Error (RMSE). The strong correlation between smoking status and premium costs, along with positive correlations with age and body mass index (BMI), suggests that premium costs increase with increasing age and weight category.
Prediksi Harga Mobil Bekas Menggunakan Algoritma Regresi Linear Berganda Dea Miftahul Huda; Gifthera Dwilestari; Ade Rizki Rinaldi; Iin Solihin
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.v6i1.10266

Abstract

The lack of information regarding used car prices creates obstacles for people in buying and selling vehicles because they don't understand the market prices that are used as a reference. This information is very important to know price predictions with the range of variables that can be considered. The aim is to process an algorithm model that is capable of carrying out statistics using appropriate techniques to make predictions. Prediction is a very important technique in decision making. The linear regression algorithm is a model building technique used to predict the value of a given dataset. In this study, a multiple linear regression algorithm was used to predict used car prices. The dataset used to create a prediction model with a linear regression algorithm was sourced from the Kaggle repository for used car prices and then the results were visualized in Rapminer. The prediction process uses a comparison of testing data and training data with a ratio of 90 training data and 10 testing data in the process of building the model and evaluating the model that has been produced. The result of the prediction process using the linear regression algorithm is a prediction model of Price 1637.49. The prediction model will be evaluated with 2 assessment indicators, namely RMSE and Relative Error. The results obtained from this model, in the Price category, the RMSE value is 1637.49 and the Relative Error value is 11.89%. And the application of the regression model to new data from the independent variables used is the attribute Age (Age) 24 X1, Kilometers (KM), 783764 X2, Horse power (HP) 100 X3, Transmission (Automaitc) 0 X4, Engine capacity (CC) 1500 regression equation Y = b1 + b2X1 + b3X2 + b4X3 + b5X4 +b6X5 +b7X6.
Implementasi Metode K-Means Clustering Untuk Menganalisa Penerima Bantuan Di Desa Palasah Mar’atun Sholihah, Oliffia; Suarna, Nana; Dwilestari, Gifthera; R, Nining
Jurnal Informatika dan Teknologi Informasi Vol. 1 No. 3: Januari 2023
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jt.v1i3.121

Abstract

Overcome welfare problems for the community through direct assistance to very poor families in Palasah Village. Related to the problems that have occurred so far, namely that the assistance program targets for poor people, many people who should not have received the assistance, turned out to be inaccurate and valid. So many residents are not registered and recorded accurately. The purpose of this study was to determine the distribution of the accuracy of aid recipients in Palasah Village, Majalengka Regency. The method used in this study is the k-means clustering method, with stages of data collection, training data and data testing that considers several criteria based on: age, economic conditions, housing conditions, and income. With the K-means clustering test k=10, it can produce davies bouldin with sequence k2=0,587, sequence k3=0,529, sequence k4=0,588, sequence k5=0,581, sequence k6=0,529, sequence k7=0,612, sequence k8=0,653, sequence k9 = 0.639, sequence k10 = 0.669, then the conclusion of the Cluster distance performance process produces the best dbi, which is 0.529 in the 3rd order. Can be implemented grouping the data for sewing equipment recipients with the K-Means clustering method, can find out which cluster residents get assistance with sewing tools in Palasah Village. Keywords: Cluster K-Means Algorithm, Social Assistance
Implementasi Metode K-Means Clustering Untuk Menganalisa Penerima Bantuan Di Desa Palasah Mar’atun Sholihah, Oliffia; Suarna, Nana; Dwilestari, Gifthera; R, Nining
Jurnal Informatika dan Teknologi Informasi Vol. 2 No. 1: Mei 2023
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jt.v2i1.123

Abstract

Untuk mengatasi masalah kesejahteraan bagi masyarakat melalui bantuan langsung pada keluarga sangat tidak mampu di Desa  Palasah. Terkait permasalahan yang terjadi selama ini yaitu tidak tepat sasaran program bantuan kepada warga yang tidak mampu, banyak warga yang seharusnya tidak mendapatkan bantuan tersebut, ternyata tidak akurat dan valid. Sehingga banyak warga yang tidak terdaftar dan terdata dengan akurat. Tujuan penelitian ini untuk mengetahui penyebaran akurasi penerima bantuan di Desa Palasah Kabupaten Majalengka. Metode yang digunakan dalam penelitian ini yaitu dengan metode k-means klustering, dengan tahapan pengumpulan data, data training dan data testing yang mempertimbangkan beberapa Kriteria berdasarkan: umur, kondisi ekonomi, kondisi hunian, serta penghasilan. Dengan uji K-means clustering k=10, dapat menghasilkan davies bouldin dengan urutan k2=0,587, urutan k3=0,529, urutan k4=0,588, urutan k5=0,581, urutan k6=0,529, urutan k7=0,612, urutan k8=0,653, urutan k9=0,639, urutan k10=0,669, maka kesimpulan dari proses Cluster distance performance menghasilkan  dbi yang terbaik yaitu 0,529 pada urutan ke 3. Dapat diimplementasikan pengelompokan data penerima alat menjahit dengan  metode K-Means clustering,  dapat mengetahui warga mana saja cluster yang mendapat bantuan alat menjahit di Desa Palasah.
Klasifikasi Data Kemiskinan Menggunakan Metode Naïve Bayes Untuk Mengetahui Tingkat Kemiskian Studi Kasus: Desa Karangasem Kecamatan Leuwimunding Majalengka Fuadi Ahmad, Cecep; Suarna, Nana; Dwilestari, Gifthera
Jurnal Informatika dan Teknologi Informasi Vol. 2 No. 2: September 2023
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jt.v2i2.190

Abstract

Kemiskinan merupakan permasalahan paling utama di Negara Indonesia, seperti hal nya di Desa Karangasem, Majalengka. Masalah ini merupakan masalah yang sangat serius bagi berlangsung nya kehidupan manusia. Banyak upaya yang sudah dilakukan untuk menangani masalah kemiskinan, seperti halnya bantuan sosial yang dilakukan oleh pemerintah dengan tujuan untuk memenuhi dan menjamin kebutuhan dasar serta meningkatkan taraf kehidupan. Namun pada kenyataan nya program bantuan tersebut belum tersebar secara merata. Penelitian ini akan melakukan klasifikasi. Metode yang digunakan untuk penelitian ini adalah metode Naïve Bayes dan menggunakan sebuah sistem komputerisasi yaitu aplikasi rapidminer. Data yang digunakan ialah data penduduk miskin yang diperoleh dari Desa Karangasem dengan menggunakan teknik data mining. Atribut yang akan digunakan dalam melakukan klasifikasi penduduk adalah Umur, Pendidikan, Pekerjaan, Penghasilan, Tanggungan, Status (Kawin/Belum Kawin). Hasil penelitian didapatkan nilai akurasi sebesar Precision sebesar 92% dan Recall sebesar 86%. Berdasarkan hal tersebut dapat dinyatakan bahwa sistem klasifikasi yang dibangun dapat gunakan sebagai bahan masukan bagi pengambil keputusan.
Metode Naïve Bayes Untuk Mengetahui Tingkat Kemiskian (Studi Kasus: Desa Karangasem Kecamatan Leuwimunding Majalengka) Fuadi Ahmad, Cecep; Suarna, Nana; Dwilestari, Gifthera
Jurnal Informatika dan Teknologi Informasi Vol. 2 No. 3: Januari 2024
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jt.v2i3.191

Abstract

Kemiskinan merupakan permasalahan paling utama di Negara Indonesia, seperti hal nya di Desa Karangasem, Majalengka. Masalah ini merupakan masalah yang sangat serius bagi berlangsung nya kehidupan manusia. Banyak upaya yang sudah dilakukan untuk menangani masalah kemiskinan, seperti halnya bantuan sosial yang dilakukan oleh pemerintah dengan tujuan untuk memenuhi dan menjamin kebutuhan dasar serta meningkatkan taraf kehidupan. Namun pada kenyataan nya program bantuan tersebut belum tersebar secara merata. Penelitian ini akan melakukan klasifikasi. Metode yang digunakan untuk penelitian ini adalah metode Naïve Bayes dan menggunakan sebuah sistem komputerisasi yaitu aplikasi rapidminer. Data yang digunakan ialah data penduduk miskin yang diperoleh dari Desa Karangasem dengan menggunakan teknik data mining. Atribut yang akan digunakan dalam melakukan klasifikasi penduduk adalah Umur, Pendidikan, Pekerjaan, Penghasilan, Tanggungan, Status (Kawin/Belum Kawin). Hasil penelitian didapatkan nilai akurasi sebesar Precision sebesar 92% dan Recall sebesar 86%. Berdasarkan hal tersebut dapat dinyatakan bahwa sistem klasifikasi yang dibangun dapat gunakan sebagai bahan masukan bagi pengambil keputusan
Analisa Dataset Penjualan Teh Menggunakan Algoritma Linear Regresi Riyana, Iis; Suarna, Nana; Dwilestari, Gifthera
Jurnal Informatika dan Teknologi Informasi Vol. 2 No. 2: September 2023
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jt.v2i2.194

Abstract

Dalam industri minuman, analisis penjualan menjadi salah satu faktor kunci untuk keberhasilan bisnis. Dalam konteks ini, analisis dataset penjualan teh dapat memberikan wawasan yang berharga bagi perusahaan teh dalam hal pengambilan keputusan strategis. Dalam penelitian ini menggunakan algoritma linear regresi untuk menganalisis dataset penjualan teh dan mengidentifikasi pola dan tren yang terkait dengan penjualan. Masalah yang dihadapi dalam analisis dataset penjualan teh adalah menemukan hubungan antara variabel penjualan dengan variabel-variabel yang mempengaruhinya, seperti harga, promosi, cuaca, dan sebagainya. Selain itu, juga perlu memahami apakah faktor-faktor ini secara signifikan berdampak pada penjualan teh dan sejauh mana dampak tersebut.  Tujuan penelitian ini adalah untuk menganalisis dataset penjualan teh menggunakan algoritma linear regresi guna menemukan hubungan antara variabel penjualan dengan variabel-variabel yang mempengaruhinya. Selain itu, peneliti bertujuan untuk mengidentifikasi faktor-faktor yang signifikan dalam memprediksi penjualan teh dan memberikan wawasan yang berharga bagi perusahaan teh. Metode dalam penelitian ini menggunakan metode algoritma linear regresi. mengumpulkan dataset penjualan teh yang mencakup variabel-variabel seperti harga teh, promosi, cuaca, dan penjualan harian
Co-Authors Abdul Ajiz Abdul Ajiz, Abdul Abdul Rauf Chaerudin Abdullah Syafii Abdullah Syafii Aby Febrian Ade Irma Purnamasari Ade Irma Purnamasari Ade Kurnia, Dian Ade Rizki Rinaldi Agis Maulana Robani Agung Nugraha agus bahtiar Ahmad Faqih Ahmad Faqih Ahmad Rifa'i Ahmad Zam Zami Aldiani, Dea Alia Cahyani, Cica Alibasyah, Aziz Amal Rois, Moh. Ichlasul Ananda Rafly Andi Suandi Anita Nur Kirana Anwar Musaddad Apriliyani, Ela Arif Rinaldi Dikananda Arifin, Bagas Adam Athhar Hafizha Luthfi Auliya Azmi Afifah, Turfa Bagas Al Haddad Bambang Siswoyo Basysyar, Fadhil Muhammad Caswadi, Caswadi Chaerudin, Chaerudin Cindyk Irawanto Dadang Sudrajat Dea Miftahul Huda Dessy Angelina Destriyanah, Riska Dian Ade Kurnia Dias Bayu Saputra Dienwati Nuris, Nisa Dienwati, Nisa Dikananda, Arif Rinaldi Dikananda, Fatihanursari Dzaffa 'Ulhaq Edi Tohidi Edi Tohidi Eka Permana, Sandy Fadhil Muhammad Basysyar Fadhil Muhammad Basysyar Fajar Fauzan, Muhammad Fajar Maulana Adji, Moh Fajria, Azzahra Moudy Fasa, Saefullah Fathurrohman Fathurrohman Fatihanursari Dikananda Faujia, Agnes Fithrah Ali, Dini Salmiyah Fuadi Ahmad, Cecep Hamonangan, Ryan Haris Abdul Hadi Herdiana, Rulli Hermawan, Bagus Hermawan, Muhammad Andi Hilya Ashfia Nabila Himawan, Irvan Hira Wahyuni Azizah Hoeriah, Dede Hoerunnisa, Anis Iin Iin Solihin Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Agustina Irma Purnamasari, Ade Irvan Himawan Jayawarsa, A.A. Ketut Karimah, Ayu Kaslani Kencana, Junaedi Surya Khoirul Huda, Muhammad Kokom Komariyah Lestari, Anjar Ayuning Martanto . Mar’atun Sholihah, Oliffia Maulana Sidiq, Cecep Mochamad Aditya Sunaryo Muhammad Abdurohman Muhammad Basysyar, Fadhil Mulyawan Mulyawan, Mulyawan Musliyadi, Mar'i Muzaki, Fazri Nana Suarna Nana Suarna Nana Suarna Narasati, Riri Narasati Nining R Nining Rahaningsih Nisa Dieanwati Nuris Nur Amalia Nur Kirana, Anita Nuraini, Asyifa Nurhakim, Bani Nurul Aini, Yuli NURUL HIDAYAH Nurwahidah, Dalilah Odi Nurdiawan Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Prihartono, Willy Puspita Maulana Arumsari R, Nining Raditya Danar Dana Raena Agustin Laeliyah Rahaditya Dasuki Ramdhan, Dadan Ramiro Firjatullah, Federicko Ranu Husna Riyana, Iis Rizki Fauzi, Ahmad Rizqy, Muhammad Enricco Rosmeri Manurung, Agnes Rudi Kurniawan Saeful Anwar Saeful, Agung Saefullah Fasa Saepu Qirom, Dani Saepudin, Asep Saepul Hadi Sagita, Ayu Salsabila, Putri Septiana, Angga Sri Suwartini Suandi, Andi Suarna, Nana Subhiyanto, Fajar Sunana, Heliyanti Suryani Dewi, Ike Susana, Heliyanti Syafi'i Syafi'i Syafi'i, Syafi'i Tati Suprapti Tohidi, Edi Tuti Hartati Umi Hayati Vibrianti, Vera Wahyudin, Edi Wulan Suci, Salwa Yubi Aqsho Ramadhan