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All Journal Jurnal Transformatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Kilat Jurnal Review Pendidikan dan Pengajaran (JRPP) Journal of Computer Science and Engineering (JCSE) Indonesian Journal of Technology, Informatics and Science (IJTIS) Transformasi EXERO : Journal of Research in Business and Economics Jurnal Kabar Masyarakat
Wardhani, Anindya Khrisna
Universitas Diponegoro
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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K-MEANS ALGORITHM IMPLEMENTATION FOR CLUSTERING OF PATIENTS DISEASE IN KAJEN CLINIC OF PEKALONGAN Anindya Khrisna Wardhani
Jurnal Transformatika Vol 14, No 1 (2016): July 2016
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v14i1.387

Abstract

In determining the consistency of health data, can use data mining techniques that can dig the hidden information from multidimensional data sets that have been obtained. In addition, data wich connected with other data can also be done by these data mining techniques. One of the data mining techniques is quite well known namely clustering. The methods are quite popular in data mining techniques that called k-means method. It is used to facilitate medical recorder for analyzing the general health situation of population groups in archiving health care data. The results of this analysis, the clustering of the disease based on age, sex, duration of disease and disease diagnosis.This research used tool Rapid Miner 5.3.Based on the data from clinic centers Kajen Pekalongan, the result of clustering is 376 items of acute and 624 unacute diseases from 1000 total of data.
SISTEM INFORMASI PEMILIHAN TEMPAT WISATA MENGGUNAKAN METODE WEIGHTED PRODUCT Anindya Khrisna Wardhani; Arlieza Anindyaputri
Indonesian Journal of Technology, Informatics and Science (IJTIS) Vol 2, No 1 (2020): IJTIS Volume 2 No 1 (Desember 2020)
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/ijtis.v2i1.5649

Abstract

Seiring dengan banyaknya tempat wisata, membuat masalah bagi wisatawan yang ingin memilih restoran. Hal ini bahkan dapat mengambil banyak waktu karena wisatawan dihadapkan pada banyak pilihan dan kurangnya informasi lengkap mengenai tempat wisata tersebut. Oleh karena itu, diperlukan sistem pendukung keputusan untuk membantu wisatawan dalam memilih tempat wisata. Dalam menentukan pemilihan tempat wisata menggunakan metode Weighted Product diperlukan beberapa kriteria menentukan tempat wisata yaitu jarak dari pusat kota, harga tiket masuk dan fasilitas. Sistem pemilihan lokasi wisata dengan metode weighted product diimplementasikan dalam sistem berbasis ponsel. Aplikasi ponsel dibuat untuk memudahkan admin dan pengguna memilih tempat wisata dengan proses yang memasukkan beberapa kriteria yang diinginkan oleh pengguna. Aplikasi ini juga di tambahkan google maps, sehingga pengguna langsung bisa mengetahui tempat wisata yang sudah menjadi pilihannya. Sistem informasi untuk memilih tempat wisata menggunakan weighted product dapat berjalan dengan baik. Pada pengujian black box, sistem dapat berjalan sesuai yang diharapkan. Pada pengujian beta, diperoleh kesimpulan bahwa  sistem yang dibangun berfungsi sesuai dengan yang diinginkan user dengan skor 78,29%. Kata kunci: weighted product, sistem pendukung keputusan, sistem informasi,
IMPLEMENTASI ALGORITMA PARTITIONING AROUND MEDOIDS DALAM PENGELOMPOKAN RESTORAN Tito Bimantoro; Anindya Khrisna Wardhani
Indonesian Journal of Technology, Informatics and Science (IJTIS) Vol 2, No 1 (2020): IJTIS Volume 2 No 1 (Desember 2020)
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/ijtis.v2i1.5651

Abstract

Banyaknya data yang hanya sebatas memberikan grafik atau statistik mengenai data restoran dapat dimanfaatkan untuk penggalian data menggunakan teknik data mining. Informasi yang dapat digali dari data restoran adalah pengelompokan kategori restoran berdasarkan jumlah menu yang terdapat pada restoran tersebut. Penelitian ini menerapkan teknik data mining menggunakan metode clustering partitioning around medoids (k-medoids) untuk menghasilkan informasi mengenai pengelompokkan kategori restoran. Algoritma clustering Partitioning Around Medoids(PAM) atau disebut juga K-Medoids adalah algoritma untuk menemukan k cluster dalam n objek dengan pertama kali secara arbitrarily menemukan wakil dari objek (medoid) untuk tiap-tiap cluster. Pengelompokan dalam penelitian ini  adalah appetizer, soup, main course, dessert, minuman dan snack. Kemudian hasil tersebut dapat dijadikan bahan atau dasar dari konsumen untuk memilih restoran berdasarkan kategori apa yang ingin konsumen makan. Berdasarkan data yang diperoleh, dihasilkan cluster yang merepresentasikan appetizer (cluster_2) 17 item, soup (cluster_1) 7 item, main course (cluster_4) 51 item, dessert (cluster_3) 11 item, minuman (cluster_5) 6 item dan snack (cluster_0) 13 item. Kata kunci: Data Mining, K- Medoids, Clustering
PENERAPAN METODE SINGLE MOVING AVERAGE UNTUK PERAMALAN HARGA CABAI RAWIT HIJAU Astrid Novita Putri; Anindya Khrisna Wardhani
Indonesian Journal of Technology, Informatics and Science (IJTIS) Vol 2, No 1 (2020): IJTIS Volume 2 No 1 (Desember 2020)
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/ijtis.v2i1.5653

Abstract

Harga cabai yang kurang menentu dan bahkan cenderung terus mengalami kenaikan pada beberapa waktu tertentu akan berakibat buruk. Oleh karena itu informasi naik turunnya harga cabai pada waktu-waktu sebelumnya, dapat menjadi variable baru yang dipertimbangkan dalam peramalan harga cabai. Melihat permasalahan tersebut di harapkan metode single moving average dapat digunakan dengan baik untuk memprediksi fluktuasi tren harga cabai, sebagai langkah antisipasi permintaan pasar. Penelitian ini bertujuan untuk membandingkan metode single moving average dengan menggunakan ordo yang berbeda. Pada metode Single Moving Average berordo 10 didapatkan nilai et 199.600, MSE 7.677 dan RMSE 14.12799. Sedangkan metode Single Moving Average berordo 5 didapatkan nilai et 118.200, MSE 3.813 dan RMSE 10.87198. Dari tersebut, diantara dua metode yang digunakan dalam peramalan harga cabai di Kota Semarang dengan mengunakan metode Single Moving Average berordo 5 dan Single Moving Average berordo 10, terbukti bahwa metode Single Moving Average berordo 5 lebih baik dibandingkan dengan metode Single Moving Average berordo 10.Kata kunci: single moving average, time series, MSE, RMSE
Application Culinary Decision Support System in Kudus City with Weighted Product Method Based on Mobile Phone Anindya Khrisna Wardhani; Erba Lutfina
Journal of Computer Science and Engineering (JCSE) Vol 1, No 1: February (2020)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.795 KB) | DOI: 10.36596/jcse.v1i1.17

Abstract

Abstract: Along with the number of restaurants located in the city of Kudus, making a problem for a consumer or tourists who want to choose a restaurant. This can even take a lot of time because consumers are faced with the many choices of restaurants. Therefore, a decision support system is needed to assist consumers or tourists in choosing a culinary place. In determining the selection of culinary places in the city of Kudus, using the Weighted Product method to determine the culinary place with several criteria: the number of menus, the average price, wifi, and corner charger. The culinary site selection system with weighted product method is implemented in the mobile phone-based system. The mobile phone application is made to facilitate the admin and user choose culinary or restaurant with the process used in determining the culinary place in the city of Kudus by using criteria inputted by the user. This app is also in add google maps, so direct users can knowing location culinary location or restaurant that has become his choice.Abstrak: Seiring dengan banyaknya restoran yang berlokasi di kota Kudus, membuat masalah bagi konsumen atau wisatawan yang ingin memilih restoran. Ini bahkan dapat mengambil banyak waktu karena konsumen dihadapkan pada banyak pilihan restoran. Oleh karena itu, diperlukan sistem pendukung keputusan untuk membantu konsumen atau wisatawan dalam memilih tempat kuliner. Dalam menentukan pemilihan tempat kuliner di kota Kudus, menggunakan metode Weighted Product untuk menentukan tempat kuliner dengan beberapa kriteria: jumlah menu, harga rata-rata, wifi, dan tempat charger. Sistem pemilihan lokasi kuliner dengan metode weighted product diimplementasikan dalam sistem berbasis ponsel. Aplikasi ponsel dibuat untuk memudahkan admin dan pengguna memilih kuliner atau restoran dengan proses yang digunakan dalam menentukan tempat kuliner di kota Kudus dengan menggunakan kriteria yang dimasukkan oleh pengguna. Aplikasi ini juga di tambahkan google maps, sehingga pengguna langsung bisa mengetahui lokasi lokasi kuliner atau restoran yang sudah menjadi pilihannya.
Penerapan Algoritma Partitioning Around Medoids Untuk Menentukan Kelompok Penyakit Pasien (Studi Kasus : Puskesmas Kajen Pekalongan) Anindya Khrisna Wardhani
KILAT Vol 6 No 1 (2017): KILAT
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (686.164 KB) | DOI: 10.33322/kilat.v6i1.661

Abstract

Heaps of data residing on health services, polyclinics, hospitals and clinics are now only limited to providing graphs or statistics on the number of patients seeking treatment. The contents of the report in the form of the illness and its report medicine information from the disease.This research applies methods of partitioning around medoids (k-medoids) to produce information about the grouping of the disease "Acute" and "NOT ACUTE" that affects many patients in Puskesmas Kajen Pekalongan. Then these results can be used as ingredients or basic health education by the local Health Department. Based on the data obtained, the resulting number of acute cluster there are 94 items, not acute cluster 906 items with a total amount of data is 1000.
Analisis Strategi Digital Marketing Di Masa Pandemi Covid-19 Anindya Khrisna Wardhani; Arina Nuraliza Romas
EXERO : Journal of Research in Business and Economics Vol 4, No 1 (2021)
Publisher : Fakultas Ekonomi, Universitas Sanata Dharma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (273.367 KB) | DOI: 10.24071/exero.v4i1.5025

Abstract

The Covid-19 pandemic has affected the global economy and environment. The main victims of the covid-19 outbreak are Micro Small and Medium Enterprises (MSMEs), especially in developing countries due to the limited use of digital technology. Digital marketing is a strategic step that can be done by MSMEs to market their products without having to violate health protocols. Research methods use qualitative methods with an analytical descriptive approach. Descriptive research by collecting data based on factors that support the object of research, while qualitative research deals with ideas, perceptions, opinions, beliefs of people to be researched and everything that cannot be measured by numbers. The results showed that the difference in marketing before the pandemic and during the pandemic lies in the use of the internet. Digital marketing has only been widely known by the public since the pandemic, but people's understanding of the use of digital marketing is still quite low and needs to be improved so that marketing is done more effectively and efficiently.
Microsoft Word Skills Development Training for Final Semester Students: Pelatihan Pengembangan Keahlian Microsoft Word Bagi Mahasiswa Semester Akhir Wardhani, Anindya Khrisna; Nur Latifah Dwi Mutiara Sari; Astrid Novita Putri
Jurnal Kabar Masyarakat Vol. 2 No. 1 (2024): Februari : JURNAL KABAR MASYARAKAT
Publisher : Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jkb.v2i1.1609

Abstract

The use of Microsoft Word skills is important in the academic world, especially for final semester students who are completing their final assignments. This community service aims to provide Microsoft Word skill development training for final semester students with a focus on increasing the efficiency and quality of preparing thesis reports. Through a series of intensive training sessions, students will be equipped with an in-depth understanding of Microsoft Word's advanced features that can support the research process and preparation of scientific documents. This training was attended by 129 participants from various universities via zoom meetings. The activity lasted for 4 hours consisting of material presentation, case studies, questions and answers and ended with closing. After participating in this activity, participants receive significant benefits in improving students' abilities so that they can make maximum use of word processing software, and can produce quality final assignment reports that meet academic standards. Apart from that, community service this time can also motivate students to develop information technology skills which will be an added value in their future careers.
PREDIKSI PENYEBARAN TUBERKULOSIS DI INDONESIA MENGGUNAKAN SINGLE MOVING AVERAGE Wardhani, Anindya Khrisna; Putri, Astrid Novita; Sari, Nur Latifah Dwi Mutiara
TRANSFORMASI Vol 19, No 2 (2023): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v19i2.386

Abstract

Penelitian ini bertujuan untuk meramalkan penyebaran Tuberkulosis (TB) di Indonesia menggunakan metode Single Moving Average (SMA) dengan tiga variasi ordo: 10, 5, dan 3, berdasarkan data jumlah penderita TB dari tahun 2000 hingga 2022. Hasil prediksi menunjukkan bahwa SMA berordo 3 memberikan kinerja terbaik dengan Mean Squared Error (MSE) sebesar 45,315, Root Mean Squared Error (RMSE) sebesar 952, dan Mean Absolute Deviation (MAD) sebesar 287,542. Model ini lebih sensitif terhadap perubahan dalam data, memberikan prediksi yang lebih akurat dibandingkan dengan ordo 5 dan 10. Kesimpulan menyoroti bahwa penggunaan SMA berordo 3 dapat memberikan informasi yang lebih akurat tentang tren penyebaran TB di Indonesia. Rekomendasi penelitian lebih lanjut mencakup eksplorasi metode analisis prediktif lainnya dan mempertimbangkan faktor-faktor tambahan yang dapat memengaruhi penyebaran TB, seperti faktor lingkungan dan kebijakan kesehatan.
Optimization of the Decision Tree Method using Pruning on Liver Disease Classification Wardhani, Anindya Khrisna; Nugraha, Ega; Ulfiana, Qonita
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4350

Abstract

The amount of data about liver disease can be used to become information that can be extracted using the decision tree data mining method. However, there is a weakness in the decision tree method, namely over-fitting the resulting tree can produce a good model in training data but normally cannot produce a good tree model when applied to unseen data. Based on experiments conducted using datasets taken from The UCI Machine Learning Repository database is the ILPD dataset which contains 583 clinical data with 10 attributes with a target output of 416 positive liver and 167 negative liver. The results show that the decision tree algorithm using pruning and without pruning has been tested showing an increase in accuracy. The results of the decision tree performance without pruning generated in the confusion matrix for the accuracy measure, which is 73.58 %. While the results of the system performance using the pruning method have an accuracy of 73.76%. Although the accuracy value is slightly adrift, it can prove that the decision tree method using the pruning method has much better accuracy. In addition, the models and rules generated by the decision tree can be used as the basis for developing a prototype application for liver disease classification.
Co-Authors Arina Nuraliza Romas Arlieza Anindyaputri Astrid Novita Putri Astrid Novita Putri, Astrid Ega Nugraha Erba Lutfina Nugraha, Ega Nur Latifah Dwi Mutiara Sari Qonita Ulfiana Sari, Yuhana Indah Tito Bimantoro Ulfiana, Qonita