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PEMETAAN DAERAH BERPOTENSI TRANSMIGRAN DI KECAMATAN KARTASURA DENGAN METODE FUZZY C-MEANS (FCM) CLUSTERING Mawar Hardiyanti; Yustina Retno Wahyu Utami; Wawan Laksito Yuly Saptomo
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 6, No 1 (2018): Jurnal TiKomSiN
Publisher : STMIK Sinar Nusantara

In an attempt to achieve the well-being of Indonesia, one of the Government's policies that need to be implemented are the deployment and implementation of the transmigration program. In General only a transmigration program offered by the Government to all societies without knowing the economic background and his family so that the transmigration program was not right on target. Based on the background of the problems in this research is how to design, build, develop and implement Fuzzy C-Means Clustering on Regional Mapping System for classifying the area potentially Homesteader in Kartasura. The data obtained by conducting interviews at the population administration of the subdistrict of Kartasura, observation, and study of the literature. In this research, the author uses secondary data. Data obtained from Reports in Kartasura Subdistrict number 2015 by BPS (Statistics Indonesia) Sukoharjo Regency. The results obtained are Fuzzy C-Means method can be applied to a system of mapping the area potentially Homesteader in Kartasura can optimize the work of the Government in the implementation of the resettlement program. Testing the cluster with Center validation methods using MPC alternate data criteria in the period the year 2014 and 2015 which States that 3 clusters are the cluster validation.Keywords: Classification, Fuzzy C-Means, Transmigration

PERMODELAN PENGETAHUAN KESIAPAN PENANGANAN BENCANA DI RUMAH SAKIT Mawar Hardiyanti; Dhomas Hatta Fudholi
Indonesian Journal of Business Intelligence (IJUBI) Vol 4, No 2 (2021): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Program Studi S1 Sistem Informasi Fakultas Komputer dan Teknik Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v4i2.1934

Bencana alam adalah peristiwa yang umumnya membawa dampak negatif. Indonesia adalah salah satu negara rawan bencana. Rumah sakit merupakan tempat rujukan pertama saat korban bencana membutuhkan perawatan. Pada dasarnya, berbagai studi yang menghasilkan pengetahuan sudah cukup banyak tersedia. Namun untuk penggunaannya pada bencana alam belum dikelola dan diterapkan dengan baik. Berdasarkan hal tersebut maka kami akan membangun medel pengetahuan kesiapan penanganan bencana di rumah sakit. Penelitian ini mengembangkan sebuah model pengetahuan berbasis ontologi untuk kesiapan rumah sakit pada penanganan bencana berdasarkan konsep tenaga kesehatan, institusi terkait, rencana darurat, dan alat. Proses permodelan ontologi pada penelitian ini terdiri dari tiga fase yaitu Konseptualisasi, Implemetasi dan Evaluasi. Pembangunan ontologi didasarkan dari hasil kuisioner yang telah diisi oleh pengurus TIM Bencana dari tiga rumah sakit di Jawa Tengah. Hasil yang didapatkan dari pengukuran ontologi yang dibuat untuk Relationship Richness sebesar 0.68, Inheritance Richness sebesar 0.18, dan Attribute Richness sebesar 0.04. Sedangkan hasil pengujian query yang dilakukan menggunakan DL Query Panel adalah sistem dengan kemapuan memberi sebuah jawaban dari gabungan ekspresi Class, object property untuk mendapatkan instancedari data Individual.

Identifikasi Wanda Janaka berbasis Deep Learning dengan Metode Convolutional Neural Network Benedictus Herry Suharto; Mawar Hardiyanti
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

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UNESCO named Wayang Kulit a "Masterpiece of Oral Intangible Heritage of Humanity" for Indonesian traditional arts. Wayang Kulit's characters show a symbol of personality, identity, image, appearance, and quality of the characters. In certain situations in the Wayang Kulit performance scene, this character has a different appearance image called Wanda. Wanda Wayang Kulit is less well-known among the younger generation. Therefore, technological creativity is needed so the younger generation can distinguish Wanda Wayang Kulit as part of the Indonesian nation's artistic and cultural literacy. One of the potential technologies that can be used is a smartphone application based on Deep Learning CNN to detect and recognize Wanda Wayang Kulit. In contrast to previous studies that have successfully used Deep Learning CNN to recognize Wayang Kulit characters, this research aims to create a CNN Deep Learning model to classify Wanda Wayang Kulit Janaka. The model uses five Wanda Wayang Kulit Janaka images from an Android smartphone camera. The results achieved from this study are the CNN deep learning model, which can classify the image of Wanda Wayang Kulit Janaka using an Android smartphone camera with an accuracy of 84%.

THE INFLUENCE OF COOPERATIVE LEARNING MODELS TO INCREASE LEARNING MOTIVATION ON OBJECT-ORIENTED PROGRAMMING MATERIALS Zega, Imanuel; Rondonuwu, Yeremia Victor; Hardiyanti, Mawar; Sarumaha, Demonius
Educational Technology Journal Vol 4 No 1 (2024): Volume 4 Nomor 1, April 2024
Publisher : Universitas Negeri Surabaya

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The world of education requires an interesting learning process for students, with the aim that the students being taught do not feel bored in following the learning process. Learning methods that use conventional methods are one of the causes of students feeling bored in the learning process. The research carried out was Classroom Action Research (PTK) with a cooperative learning model applied to third-semester students in the Information Systems study program at Unversitas Pignatelli Triputra. Cooperative learning is a teaching method that involves students studying in small groups to achieve common goals when completing assignments. It is based on the idea that when students work together and exchange knowledge, they learn more effectively. Based on the results of the data analysis that has been carried out, it is known that the initial reflection scores achieved by students were only 22.2% of students who achieved completeness, 55.5% in cycle I and cycle II, and as much as 83.3%. This shows that the use of appropriate teaching strategies can produce real improvements and help students learn enthusiastically and achieve the desired goals.

Sistem Penjadwalan Karyawan dengan Algoritma Genetika Fajarlestari, Maria Karmelia; Hardiyanti, Mawar
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Employee scheduling is a complex problem in Human Resource Management (HRM) that significantly impacts operational efficiency. This study develops an employee scheduling system using a genetic algorithm. The employee schedules are constructed by considering scheduling rules and various components such as the number of days, shifts, employee quality, and scheduling requests. The genetic algorithm, proven effective in solving various optimization problems, is employed to generate optimal schedules through the processes of selection, crossover, and mutation. The results indicate that the genetic algorithm can effectively produce employee schedules, with fitness values indicating improved schedule quality as iterations increase. The findings of this study are anticipated to be useful in HRM, aiming to improve both employee efficiency and satisfaction.

Identifikasi Wanda Janaka berbasis Deep Learning dengan Metode Convolutional Neural Network Suharto, Benedictus Herry; Hardiyanti, Mawar
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.3.2023.226-240

UNESCO named Wayang Kulit a "Masterpiece of Oral Intangible Heritage of Humanity" for Indonesian traditional arts. Wayang Kulit's characters show a symbol of personality, identity, image, appearance, and quality of the characters. In certain situations in the Wayang Kulit performance scene, this character has a different appearance image called Wanda. Wanda Wayang Kulit is less well-known among the younger generation. Therefore, technological creativity is needed so the younger generation can distinguish Wanda Wayang Kulit as part of the Indonesian nation's artistic and cultural literacy. One of the potential technologies that can be used is a smartphone application based on Deep Learning CNN to detect and recognize Wanda Wayang Kulit. In contrast to previous studies that have successfully used Deep Learning CNN to recognize Wayang Kulit characters, this research aims to create a CNN Deep Learning model to classify Wanda Wayang Kulit Janaka. The model uses five Wanda Wayang Kulit Janaka images from an Android smartphone camera. The results achieved from this study are the CNN deep learning model, which can classify the image of Wanda Wayang Kulit Janaka using an Android smartphone camera with an accuracy of 84%.

Optimizing Sentiment Analysis of Digital Wayang Viewer Comments using SMOTE and the Naïve Bayes Algorithm hardiyanti, mawar; Fajarlestari, Maria Karmelia
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5002

Wayang performances are an integral part of Indonesia’s rich cultural heritage. This traditional art form has been deeply rooted in Indonesian society for centuries, evolving through live performances and, more recently, through rapid digital adaptations—including presentations on online platforms such as YouTube. In the digital age, YouTube has become a leading platform for video sharing, allowing audiences to enjoy wayang performances without being physically present. However, data from the Central Bureau of Statistics on Socio-Cultural Affairs indicates a decline in interest among younger generations in traditional arts such as wayang. This highlights the need for innovative and relevant approaches to reintroduce this cultural heritage to them. Sentiment analysis based on viewer comments offers an effective way to identify audience opinions—whether positive, negative, or neutral. Comment data were collected using web scraping techniques with Selenium WebDriver, enabling efficient data extraction. The collected data then underwent preprocessing, including case folding, tokenization, and stopword removal, to prepare it for classification. The Naïve Bayes algorithm was employed to categorize comments into positive, negative, or neutral sentiments. Preliminary results revealed that 51.6% of comments were positive, 42.3% neutral, and 6.0% negative. Model evaluation using K-fold cross-validation yielded an accuracy of 0.98 ± 0.01, a precision of 0.99 ± 0.01, and a recall of 0.72 ± 0.11 without applying SMOTE. After applying SMOTE, recall improved to 0.80 ± 0.05. This study contributes to the development of more accurate sentiment analysis models in the context of social media and underscores the importance of techniques like SMOTE in addressing class imbalance issues.

Pengaruh Antarmuka Pengguna terhadap Pengalaman Pengguna pada Aplikasi Financial Technology di Indonesia Rondonuwu, Yeremia Victor; Hardiyanti, Mawar
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12215

Penggunaan aplikasi financial technology (FINTECH) telah menjadi tren utama dalam industri keuangan Indonesia, memberikan kemudahan dalam transaksi seperti pembayaran, investasi, hingga pinjaman. Namun, kesuksesan aplikasi fintech tidak hanya ditentukan oleh fungsionalitasnya, tetapi juga oleh desain antarmuka pengguna (UI) yang berdampak signifikan terhadap pengalaman pengguna (UX). Penelitian ini bertujuan mengeksplorasi hubungan antara desain UI dan UX pada aplikasi fintech dengan pendekatan gabungan (mixed-methods). Pendekatan kualitatif dilakukan melalui wawancara mendalam untuk memahami persepsi pengguna, sementara pendekatan kuantitatif melibatkan survei daring terhadap 266 responden guna mengukur hubungan empiris antara desain UI dan UX. Analisis data menggunakan metode statistik deskriptif dan tematik, serta teknik machine learning untuk mendeteksi pola interaksi pengguna yang tidak terungkap melalui analisis konvensional. Hasil penelitian menunjukkan bahwa desain UI yang intuitif meningkatkan kepuasan pengguna, mempercepat adaptasi, serta mendorong keterlibatan berkelanjutan. Temuan ini menyoroti pentingnya elemen visual, navigasi efisien, dan kompatibilitas teknologi dalam membangun pengalaman pengguna yang positif. Mayoritas responden adalah wanita (65,8%) dengan rentang usia dominan di bawah 25 tahun, menggunakan aplikasi seperti GoPay, ShopeePay, dan Dana. Studi ini berkontribusi pada literatur UI/UX dengan memberikan rekomendasi desain berbasis data yang dapat diterapkan oleh pengembang untuk meningkatkan retensi pengguna serta mendukung adopsi teknologi keuangan yang lebih inklusif dan berkelanjutan. Kata kunci: antarmuka pengguna, pengalaman pengguna, Financial Technology

Sistem Penjadwalan Karyawan dengan Algoritma Genetika Fajarlestari, Maria Karmelia; Hardiyanti, Mawar
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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

Employee scheduling is a complex problem in Human Resource Management (HRM) that significantly impacts operational efficiency. This study develops an employee scheduling system using a genetic algorithm. The employee schedules are constructed by considering scheduling rules and various components such as the number of days, shifts, employee quality, and scheduling requests. The genetic algorithm, proven effective in solving various optimization problems, is employed to generate optimal schedules through the processes of selection, crossover, and mutation. The results indicate that the genetic algorithm can effectively produce employee schedules, with fitness values indicating improved schedule quality as iterations increase. The findings of this study are anticipated to be useful in HRM, aiming to improve both employee efficiency and satisfaction.

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