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All Journal PROSIDING Prosiding Seminar Nasional Indonesian Journal of Applied Informatics Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat GERVASI: Jurnal Pengabdian kepada Masyarakat International Journal of Economics, Business and Accounting Research (IJEBAR) Journal of Intelligent Decision Support System (IDSS) Multicience INTELEKTIVA Muria Jurnal Layanan Masyarakat Jurnal Teknologi Informasi Cyberku Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Proceeding of International Conference Health, Science And Technology (ICOHETECH) SATIN - Sains dan Teknologi Informasi

Wiji Lestari

Universitas Duta Bangsa Surakarta

Author-ID : 2665326

Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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DESAIN PROTOTIPE ALAT BANTU KLASTERISASI GAYA BELAJAR DAN KECERDASAN MAJEMUK BERBASIS JST KOHONEN Stefanus Santosa; Wiji Lestari Panjidang
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 2 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.633 KB)

Sistem pembelajaran yang mampu mendukung peningkatan pengalaman belajar siswa dan menciptakan kemudahan bagi guru untuk merancang strategi pembelajaran yang mampu beradaptasi dengan karakteristik siswa masih menjadi tantangan besar dunia pendidikan. Pembelajaran adaptif perlu mempertimbangkan gaya belajar dan kecerdasan majemuk siswa yang berbeda-beda dan unik. Data mining dan machine learning mampu memberikan solusi atas masalah tersebut. Penelitian ini mengusulkan suatu Desain prototipe Alat Bantu Klasterisasi Gaya Belajar dan Kecerdasan Majemuk Berbasis Jaringan Syaraf Tiruan (JST) Kohonen yang diharapkan dapat menjadi acuan pengembangan LMS yang mampu memetakan pembelajar sehingga memungkinkan siswa untuk memperoleh pelayanan pembelajaran secara khusus dan unik sesuai dengan karakteristiknya dan memudahkan guru dalam penyusunan strategi pembelajaran.

Klasterisasi Kecerdasan Majemuk Siswa Berbasis Jaringan Syaraf Kohonen Guna Mendukung Adaptive Elearning Stefanus Santosa; Wiji Lestari Panjidang; Yonathan Purbo Santosa
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.09 KB)

Learning strategies are often applied without considering the unique and different characteristics of the learner's intelligence. This causes students to have difficulty understanding the material, not focused, bored, decreased motivation, frustration, and various other learning difficulties. The efforts to create student-oriented learning strategies can be done with adaptive elearning. Adaptive elearning system requires recognition function to cluster the intelligence of the learner when learning takes place. This study shows that Kohonen's Artificial Neural Network can be used for mapping students based on multiple intelligences. The results showed that there were 8 clusters with different intelligence compositions. There is no cluster that has a single intelligence. Intrapersonal intelligence is almost owned by 90% of students, while the lowest is visual-spatial intelligence, which is only 23.33%. In order to create a learner-oriented learning process, this clustering method should be embedded in an adaptive elearning system.

Implementasi Data Mining Algoritma K-Means untuk Clustering Penyakit di RS Panti Waluyo Surakarta Anggit Nurhidayah; Wiji Lestari; Eko Purwanto
Jurnal DutaCom Vol 15 No 2
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/dutacom.v15i2.2009

Kebutuhan informasi dan pengetahuan baru untuk pendukung keputusan Rumah Sakit sangat diperlukan, ancaman gangguan kesehatan mulai diutarakan oleh badan kesehatan dunia dan menjadi fokus pemerintahan dalam usaha peningkatan kesejahteraan kesehatan masyarakat. Untuk itu diperlukan pengelompokan penyakit untuk mengetahui pola / jenis gangguan kesehatan dengan jumlah banyak maupun sedikit. Dikarenakan jumlah data rekam medis dan variabel data yang banyak maka dibutuhkan metode untuk mempermudah pengelompokan penyakit. Dengan pendekatan pengklasteran K-means, pembagian kelompok penyakit dapat dilakukan berdasarkan 3 variabel yaitu umur (vu), kode penyakit (vi) dan kecamatan (vk). Pada penelitian ini dilakukan pengklasteran menggunakan algoritma K-means yang diimplementasikan kedalam kode bahasa pemrograman. .Hasil dari pengelompokkan direpresentasikan dalam bentuk grafik sehingga pihak eksekutif lebih mudah memahami hasil pengelompokan data tersebut. Dari proses pengelompokan 200 data rekam medis periode rebruari 2019 didapatkan 3 kelompok yaitu cluster 1 dengan penyakit tertinggi Chronic ischaemic heart disease, unspecified usia terbesar lansia dengan wilayah wilayah laweyan, cluster 2 penyakit tertinggi Fever, unspecified usia terbesar dewasa dengan wilayah terbanyak laweyan, dan cluster 3 dengan penyakit tertinggi Low back pain usia lansia dengan wilayah terbanyak banjarsari dan laweyan.

ANALYSIS OF THE ASSET BASED COMMUNITY DEVELOPMENT (ABCD) MODEL FOR ANGKRINGAN 5.0 WIJI LESTARI; HERLIYANI HASANAH; RUDI SUSANTO
INTERNATIONAL JOURNAL OF MULTI SCIENCE Vol. 3 No. 03 (2022): INTERNATIONAL JOURNAL OF MULTISCIENCE - SEPTEMBER - DESEMBER 2022 EDITION
Publisher : CV KULTURA DIGITAL MEDIA

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The study aims to apply the Asset Based Community Development (ABCD) method for the development of angkringan into the angkringan 5.0 model. The method used is to use the stages in ABCD, namely are inculturation, discovery, design, define and reflection. Collecting data using literature study, observation and questionnaires. The results of the ABCD method analysis can provide an illustration that angkringan assets play an important role in business development.

IMPLEMENTATION FUZZY INFERENCE SYSTEM (FIS) FOR IDENTIFICATION IT ENTERPRENEURSHIPS BASED ON STUDENTS POTENTIAL WIJI LESTARI; INDRA HASTUTI; SRI SUMARLINDA
JURNAL EKONOMI, SOSIAL & HUMANIORA Vol 3 No 11 (2022): INTELEKTIVA : JURNAL EKONOMI, SOSIAL DAN HUMANIORA - EDISI JULI 2022
Publisher : KULTURA DIGITAL MEDIA ( Research and Academic Publication Consulting )

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This study aims to produce Fuzzy Inference System for mapping information technology (IT) entrepreneurship of students potential. This system application will acquire competence and knowledge of the IT entrepreneurs. This study will provide benefits to further increase the interest and motivation of students to become IT entrepreneurs. Results in the first phase is the mapping Entrepreneurships of information technology into the 5 types technopreneurships namely Software Application Developer, Data Analyst, Computer System & Network Engineer, Graphics Designer & Animator and Multimedia System Developer. Mapping based Academic Potential Test and Personal Characteristics (Entrepreneurships values). The results of the mapping used as domain expertise to develope systems based on Fuzzy Inference System. Result in second phase is the Mamdani Fuzzy Inference System with knowledge base, rule of system and architecture of systemfor identification IT Entrepreneurships potential of students based on Academic Potential Test and characteristics personal (entrepreneurships values).

Decision support system for lecturer publication mapping using k-means clustering method Sri Sumarlinda; Wijiyanto Wijiyanto; Wiji Lestari
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 4 (2022): Desember: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Publication is an important tridharma activity for lecturers. This study aims to produce a clustering model using the K-Means algorithm which was built for ease of operation of publications. The method used is research and development which includes the stages of data collection, data preprocessing, clustering process and cluster analysis. The input data consists of 87 with 8 attributes, namely the number of journal articles indexed by Sinta, the number of journal articles indexed by Scopus, the number of citations in Scopus, the H-index in Scopus, the number of articles in indexed journals in Google Scholar, the number of citations in Google Scholar, the H-index in Google Scholar and H-index10 in Google Scholar. The K-Means algorithm is used with 3 clusters and 100 epochs. The clustering results are divided into 3 clusters, namely cluster 1 with 17 members, cluster 2 with 32 members and cluster 3 with 38 members. Clustering with 5 clusters produces cluster 1 with 5 members, cluster 2 with 12 members, cluster 3 with 20 members, cluster 4 with 18 and cluster 5 with 32 members. The results of the cluster analysis show that the clustering process with 3 clusters is improved and the academic application is better than clustering with 5 clusters.

Application Of Mathematical Morphology Algorithm For Image Enhancement Of Breast Cancer Detection Wiji Lestari; Sri Sumarlinda
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 1st International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

This study aims to produce an image processing application using Mathematical Morphology to improve the quality of the digital image for breast cancer detection. Medical image is an image produced or used in the medical field. Improving medical image quality is very useful for diagnosis and advanced image processing. Breast healthy is important for women. Breast cancer is the main killer for women. Biomedical breast image data is secondary data. The next process is the initial processing, which is processing that is related to pixel size, gray scale, and so on. The improvement of medical image in this study uses the Mathematical Morphology method which consists of Dilation, Erosion, Opening (Erosion-Dilation) and Closing (Dilation-Erosion) processes. The expected results of this research are medical digital images that have improved their quality as a result of Dilation, Erosion, opening and closing processes.

Clinical Decision Support System for Mapping of Blood Pressure and Heart Rate Sri Sumarlinda; Wiji Lestari
Proceeding of International Conference on Science, Health, And Technology 2021: Proceeding of the 2nd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Blood pressure has influence on cardiovascular diseases. This study aims to develope clinical decision support system (CDSS) model which non rule based system. The model eas improved using data mining function, especially clustering. K-Means algorithm was used to clustering 120 data and 4 attributes{ age, obesity, sistolic, diastolic and heart rate The clustering process used 500 epoches and 3 cluster. The result of clustering produced 3 cluster. Cluster 1 is higher risk, cluster 2 is medium risk and cluster 3 is normal or lower risk.

Performance Analysis of Solar Panels in Tropical Region: A Study Case in Surakarta Indonesia Rudi Susanto; Wiji Lestari; Herliyani Hasanah
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 3rd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

The performance of solar panels affects the utilization of solar energy for daily life. This study aims to carry out the measurement of the performance of solar panels in Surakarta City is located between 110 ° 45 '15" and 110 ° 45" 35" East Longitude and between 7 ° 36" and 7 ° 56" South Latitude. Research used solar panels, current sensors, voltage sensors, temperature sensors, solar irradiance sensors, humidity sensors, Arduino and Labview. The solar panels 20 WP is used in the experiment. The measurement results obtained that the maximum energy value per day produced is 165 Wh and a minimum of 76.8 Wh with an average of 109.1 Wh. Temperature measurements were carried out in the range 37.2 0 C to 41.0 0C which is the normal temperature for PV operations. The average irradiation measurement is 1834.3 W/m2 while the average humidity is 32.5%. The relationship between energy and temperature, energy with solar irradiance and energy with humidity find using Pearson Product Moment Correlation (PPMC). The result show that the effect of temperature and solar irradiance were more significant than humidity.

Studi Komparatif Model Klasifikasi Kerentanan Penyakit Jantung Menggunakan Algoritma Machine Learning Wiji Lestari; Sri Sumarlinda
SATIN - Sains dan Teknologi Informasi Vol 9 No 1 (2023): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Penyakit jantung merupakan salah satu penyebab kematian baik di dunia maupun Indonesia. Perhatian awal dari penyakit jantung akan memudahkan pencegahan dan penyembuhanya. Tujuan penelitian ini adalah melakukan analisis komapratif model klasifikasi dengan berbagai algoritma machine learning untuk kerentanan penyakit jantung. Dataset diambil dari UCI machine Learning Resipatory dengan 300 data training dan 100 data testing. Parameter klasifikasi terdiri dari age, sex, systolic blood pressure, cholesterol, thalach, oldpeak dan slope, serta labelnya cardio. Model klasifikasi dibangun dengan algoritma NaÃ¯ve Bayes, K-Nearest Neighbor (KNN), Decision Tree, random Forest, Backpropagation, Logistic Regression dan Support Vector machine (SVM). Hasil model klasifikasi dari pengukuran accuracy didapatkan NaÃ¯ve Bayes (79,00%), KNN (63,00%), Decision Tree (66,00%), Random Forest (77,00%), Backpropagation (80,00%), Logistic Regression (81,00%) dan SVM (80,00%). Dari analisis komparatif pegukuran parameter accuracy, precision, recall dan F1 score maka model klasifikasi dengan algoritma Logistic Regression dan backpropagation menghasilkan performa terbaik.

Co-Authors Anggit Nurhidayah Dimas Agih Ramadhan Hafizh Hanan Supriyanto Hasanah, Herliyani INDRA HASTUTI Indra Hastuti Indra Hastuti Novemy Triyandari Nugroho Purwanto, Eko Rudi Susanto Singgih Purnomo Sri Sumarlinda Sri Sumarlinda Stefanus Santosa Wijiyanto Yonathan Purbo Santosa

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