Article Per Year (5 Year)
p-Index From 2020 - 2025
2.147
P-Index
This Author published in this journals
All Journal Jusikom : Jurnal Sistem Komputer Musirawas JUTIM (Jurnal Teknik Informatika Musirawas) JUSIM (Jurnal Sistem Informasi Musirawas) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pengabdian kepada Masyarakat Nusantara RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer BEES: Bulletin of Electrical and Electronics Engineering Innovative: Journal Of Social Science Research Jurnal Medicare
Wulandari, Cindi
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Nursing Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Penentuan Asosiation Rule Pada Penjualan Produk UMKM Tugu Mulyo Menggunakan Metode Apriori Wulandari, Cindi; Sunardi, Lukman; Syaifudin, Pebrian
Bulletin of Computer Science Research Vol. 4 No. 1 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i1.303

Abstract

Pondok Roti has many existing variants, ranging from chocolate bread, mocca bread, round bread, donut bread, coconut bread, strawberry bread and pineapple bread, green bean bread, birthday cake, burgers, hot dogs, pizza, so the bread that is produced must be right so that the bread can be sold out without any stale and moldy bread because it is not sold. The number of unsold breads will harm the business owner. Transactions that occur in a day are quite a lot in this bread business. Sales transactions are still recorded manually using excel, and existing data has not been managed properly to become new information that can help the management in bread production. The many types and flavors of bread make it easy for buyers to choose and buy the bread they want and like. Looking at existing transaction data, it can be seen that buyers prefer certain flavors. Knowledge Discovery in Databases (KDD) is used to explain how the process of extracting information hidden in the database. Knowledge Discovery in Databases (KDD) and data mining are related to each other. This research uses the apriori algorithm to get a rule base for purchasing products at Pondok Roti stores. The apriori algorithm will later be used to find the most frequent combination of an itemset. Research data will be simulated to get the best rule base using the Weka application. The results of the research are in the form of association rules on the sale of Tugu Mulyo MSME products.
APLIKASI SIMULASI TES CAT (COMPUTER ASSISTED TEST) UNTUK CALON PNS/ASN BERBASIS WEB MOBILE Wulandari, Cindi; Yogastara, Edo
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 6 No 2 (2021): JUTIM (Jurnal Teknik Informatika Musirawas) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v6i2.1499

Abstract

Masalah berdasarkan hasil observasi yang telah dilakukan peneliti bahwa sebagian besar calon peserta CPNS masih mengalami kesulitan dalam menemukan simulasi tes CAT yang mudah, efisien serta membantu mereka untuk menambah pengetahuan sebelum pelaksanaan tes yang sesungguhnya. Melihat kenyataan yang seperti ini tidak heran banyak sekali calon ASN yang memperoleh nilai rendah dalam tes CAT. Sehingga peneliti membuat sebuah penelitian yang bertujuan untuk membangun sebuah system web mobile yang dapat melakukan proses tes CAT secara online. Penelitian yang menggunakan metode kualitatif biasanya memperoleh data yang dibutuhkan lewat berbagai cara, dimulai dari wawancara, observasi, ataupun pemeriksaan dokumen. Perbedaan dari metode ini dengan metode yang lainnya adalah lingkup penelitian yang lebih terbatas, sehingga memungkinkan peneliti untuk melakukan penelitiannya secara lebih mendalam. Pengembangan sistem pada penelitian ini yaitu menganalisis data yang digunakan dengan menggunakan SDLC (System Development Life Cycle) atau siklus hidup pengembangan sistem. Hasil penelitian menunjukkan bahwa program mengenai Sistem Informasi Simulasi Tes CAT (Computer Assisted Test) untuk calon PNS/ASN adalah aplikasi simulasi Tes CAT secara online dengan menggunakan Bahasa pemrograman PHP dan database MySQL serta Adobe Dreamweaver CS5 sebagai media penulisan listing program. Simulasi tes CAT secara online ini dirasa cukup membantu calon peserta CPNS dan memiliki efisiensi yang tepat, memiliki harga yang murah, dapat menambah pengetahuan calon peserta tes CPNS, serta hasil yang akurat dan dalam waktu yang cepat. Simulasi tes CAT ini juga dapat diakses di mana saja melalui media internet dan didukung dengan teknologi mobile phone
PERANCANGAN SISTEM PENGARSIPAN DIGITAL PADA KANTOR ATR/BPN KAB. MUSI RAWAS BERBASIS WEB RESPONSIF Wulandari, Cindi; Gunawan, Indra; Elmayati, Elmayati; Rusdiyanto, Rusdiyanto; Rizki, Fido
JUSIM (Jurnal Sistem Informasi Musirawas) Vol 8 No 1 (2023): JUSIM (Jurnal Sistem Informasi Musirawas) JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusim.v8i2.2211

Abstract

Penelitian ini bertujuan untuk membantu Kantor ATR/BPN Kabupaten Musi Rawas dalam menangani pengarsipan dokumen penting dalam bentuk digital. Penggunaan metode pada penelitian ini untuk pembangaunan sistem pengarsipan digital ini menggunakan metode waterfall yang meliputi identifikasi kebutuhan, analisa kebutuhan, perancangan sistem, perancangan perangkat lunak. Perancangan sistem pengarsipan digital ini menggunakan tool UML dan pemrograman sistem menggunakan php serta perancangan database menggunakan MySQL. Hasil penelitian menunjukan bahwa program Sistem pengarsipan digital pada ATR/BPN kabupaten Musi Rawas menggunakan bahasa pemrograman PHP dan database menggunakan MySQL. Penyimpulan yang didapat pada penelitian ini yaitu dapat membantu pihak ATR/BPN Kabupaten Musi Rawas dalam digitalisasi arsip dokumen penting dari kehilangan maupun kerusakan dari dokumen tersebut.
Prototype Sistem Informasi Publik dan Prediksi Produksi Karet Menggunakan Metode Naive Bayes Berbasis Website Wulandari, Cindi; Rizki, Fido; Lestari, Ayu
BEES: Bulletin of Electrical and Electronics Engineering Vol 3 No 1 (2022): July 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v3i1.1532

Abstract

The problem in this research is currently, public information to every village community is not carried out effectively because public information is carried out directly so that people who want to know village information must come directly to the Ngestiboga Village office 1. For the process of serving the letters needed by the community through a long process. The community must take care of, for example, a cover letter from the head of the hamlet and then forward it to the Ngestiboga 1 Village Office, where to carry out the service process to take care of the letters is difficult for the community because it is a long process. This study used data collection methods by conducting observations in Ngestiboga 1 Village, Jayaloka District, interviews with staff or the head of Ngestiboga 1 Village, Jayaloka District and literature on books related to the author's title, The results of the research are Public Information System Design and Rubber Production Prediction, the application is built using the PHP programming language with MYSQL as the application database and the application interface is built based on a Website and nave Bayes will be used as an algorithm for rubher production prediction. It can be concluded that the system can provide information and public services online to the community of Ngestiboga I Village, Jayaloka District and apply the Naive Bayes Algorithm so as to produce information on rubber production in the future.
THE RELATIONSHIP OF FAMILY SUPPORT WITH COMPLIANCE WITH A LOW SALT DIET IN HYPERTENSION PATIENTS IN POSBINDU KEMUNING, MARGAHAYU DISTRICT, BEKASI CITY, 2023 Wulandari, Cindi; Simamora, Rotua Suriany Simamora; Dinda Nur Fajri
Jurnal Medicare Vol. 3 No. 3 (2024): JULY 2024
Publisher : Rena Cipta Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62354/jurnalmedicare.v3i3.82

Abstract

Hypertension or high blood pressure is a condition in which blood pressure is above the normal limit of 120/80 mmHg. The normal limit of blood pressure is less than 130/85 mmHg. If the blood pressure is more than 140/90 mmHg then it is declared hypertension. This study aims to determine the relationship between family support and low-salt diet adherence in hypertensive patients at Posbindu Kemuning, Margahayu Village, Bekasi City in 2023. The research method used is quantitative with crossectional analytical research type.The population in this study was hypertensive patients at productive age, aged 15-64 years at Posbindu Kemuning, Margahayu Village, totaling 90 people. Data collection technique using a simple type of random sampling based on inclusion criteria and obtained a sample of 73 respondents. The results of the study found that with a significant level of 95% or a value of α 5% (0.05) the Chi Square test results obtained p-value (0.000   ) < a value of α (0.05). This indicates that H0 is rejected. The conclusion of the study is that there is a relationship between family support and adherence to a low-salt diet in people with hypertension at Posbindu Kemuning, Margahayu Village, Bekasi City in 2023.
Analisis Sentimen Aplikasi Youtube di Google Play Store Menggunakan Machine Learning Alga, Jimmy; Wulandari, Cindi; Intan, Bunga
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 4 No. 4 (2024): RESOLUSI March 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v4i4.1750

Abstract

YouTube users can create, watch, and share videos for free. Interaction between viewers occurs through the comment feature, which can be positive or negative. The frequent appearance of negative comments on the youtube application on the google play store can have an effect on these accounts. But to find out how much negative comments on the account are needed, an SVM algorithm is needed.  This study aims to determine the sentiment towards the youtube application on the google play store using Machine Learning with the SVM algorithm. The data taken is 4996 review data which is then preprocessed so that the remaining data becomes 4993 data that can be processed. Data labelling is done automatically based on the review rating score. The results of data labelling are divided into 3 classes, namely positive classes as many as 1083, negative classes as many as 3365 and neutral as many as 545. Classification and evaluation are carried out using the SVM method. Based on the training and testing data comparison value of 9: 1, the results obtained an accuracy rate of 75% then negative class precision of 76% and negative class recall of 97% and K-Fold Cross Validation testing using a value of K = 10 with an average accuracy of 0.75 or 75%.
Penerapan Algortima Support Vector Machine (SVM) Untuk Prediksi Tingkat Kelulusan Siswa SMA Wulandari, Cindi; Bimastari Aviani, Tri Hasanah; Rian Saputra
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 4 No. 4 (2024): RESOLUSI March 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v4i4.1753

Abstract

Graduation is the desire of every student to be able to complete their studies, and to achieve graduation, students must complete stages such as taking 6 semesters of learning with a school exam score for each subject above 70, and this is a rule in the school. In this study, researchers used student data for the 2022/2023 school year, which researchers took in senior high school number one Lubuklinggau. The method used by the researchers is data mining. Data mining is a term used to describe knowledge discovery in databases. The algorithm the researchers use to predict graduation is the Support Vector Machine (SVM) algorithm because it is able to predict good graduation. In predicting graduation, the accuracy value is 98.81% for XIIth grade students, 96.49% for XIth grade students, and 98.25% for Xth grade students.
Penerapan Metode CNN (Convulution Neural Network) Dalam Klasifikasi Buah Putra, Fathan Aldira; Irawan, Davit; Wulandari, Cindi
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6121

Abstract

Fruit type classification plays an important role in supporting the efficiency of distribution, sorting, and stock management processes in the agriculture and food industry. The use of technology in various aspects of life is growing rapidly, including in agriculture and agro-processing. Fruit type classification is an important stage in the fruit supply chain, starting from farmers to consumers. Traditionally, fruit type classification is done manually by human labor, which can be error-prone and time-consuming. With the advancement of technology, especially the development of Convolutional Neural Network (CNN) in deep learning, there is an opportunity to automate and improve the accuracy of the fruit type classification process based on images. Convolutional Neural Network (CNN) is one of the methods in deep learning that has proven effective in image processing and pattern recognition. This method has provided impressive results in various applications, including object classification in images. The purpose of this research is to find out how the architecture and results of the Convolutional Neural Networks (CNN) algorithm for image classification of fruit types. The method used is CNN with different epoch values on each training data. Training data is 9000 and testing data is 100, and validation data is 1000 data. The results obtained quite high accuracy training which reached 82.42% and accuracy validation reached 87.50%. from these results it can be concluded that the model is included in good accuracy and succeeded in identifying types of fruit when testing with test data.
PREDIKSI PENJUALAN KASUR DAN AMBAL DI TOKO RAJA AMBAL MENGGUNAKAN METODE TRIPLE EXPONENTIAL SMOOTHING Alhadi, Muhammad Reyhan; Karman, Joni; Wulandari, Cindi
JUSIM (Jurnal Sistem Informasi Musirawas) Vol 10 No 1 (2025): JUSIM : Jurnal Sistem Informasi Musi Rawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusim.v10i1.2584

Abstract

Dalam era globalisasi ini, persaingan usaha yang sejenis sangat ketat. Agar dapat bersaing suatu usaha harus mempunyai suatu kekuatan seperti harga jual yang bersaing, ketersediaan barang, promosi, dan lain-lain. Penelitian ini bertujuan untuk memprediksi kebutuhan stok barang di Toko Raja Ambal, Lubuklinggau, menggunakan metode Triple Exponential Smoothing. Penelitian memanfaatkan data penjualan dari Januari 2022 hingga Desember 2024 untuk menghasilkan prediksi kebutuhan stok secara akurat. Dengan menggunakan alat peramalan ini, toko diharapkan dapat mengelola persediaan secara optimal, menghindari risiko kekurangan atau kelebihan stok. Evaluasi kinerja model dilakukan menggunakan metrik seperti MAE, RMSE, dan MAPE, yang menunjukkan bahwa metode ini mampu memberikan hasil yang andal dalam mengidentifikasi pola musiman dan tren penjualan. Penelitian ini memberikan kontribusi signifikan terhadap pengambilan keputusan strategis dalam manajemen inventaris dan efisiensi operasional toko.
Analisis Sentimen Aplikasi Spotify Pada Ulasan Pengguna di Google Play Store Menggunakan Metode Support Vector Machine Wulandari, Cindi; Sunardi, Lukman; Hasbiana, Hasbiana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1762

Abstract

The Spotify app makes it easy for users to listen to their favorite songs. Usually the Spotify App is accessed on a smartphone so that it can be played at any time.  Today's digital generation can use technology in the form of music, music can affect human feelings and thoughts. The increasing number of Spotify application users on the Google Play Store, raises a variety of user reviews of the application. These reviews can be in the form of positive or negative comments. Addressing this, it is necessary to conduct sentiment analysis in order to provide a deeper understanding of user perceptions and grouping of user reviews of the Spotify application. Sentiment analysis is a case study of opinions, feelings, and emotions expressed in texs. The number of diverse reviews requires classification of reviews into positive and negative classes using the Support Vector Machine method. The purpose of this research is so that it can be examined to what extent the positive and negative reviews can be used as a reference in building the Spotify application to be even better. Object classification is done based on training data that uses the closest distance or similarity to the object for convenience. Using 5000 relevant review data from December 2023 to January 2024. After the labelling stage is carried out into positive and negative classes, there are 3193 positive and 1347 negative comments. The results of sentiment analysis testing using the Support Vector Machine method resulted in an accuracy of 85%, precision 86%, recall 92% and f1-score 89%.
Co-Authors Alga, Jimmy Alhadi, Muhammad Reyhan Andri Anto Tri Susilo, Andri Anto Aprillian, Alif Armanto, Armanto aviani, tri hasanah bimastari AYU LESTARI Baharsyah, Efrico Fernando Bimastari Aviani, Tri Hasanah Daulay, Nelly Khairani Dinda Nur Fajri Dwi Budi Santoso Elmayati, Elmayati Hasbiana, Hasbiana Hidayat, Asep Toyib Intan, Bunga Irawan, Davit Karman, Joni Lestari, Novi MARIA BINTANG Maya Sari, Wisdalia Putra, Fathan Aldira rian saputra Rizki, Fido Rusdiyanto Rusdiyanto, Rusdiyanto Simamora, Rotua Suriany Simamora sobri, ahmad Sunardi, Lukman Syaifudin, Pebrian Wijayanti, Dede Yogastara, Edo