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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal Mandiri IT Parta: Jurnal Pengabdian Kepada Masyarakat
Dewi, Ni Wayan Jeri Kusuma
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

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EVALUASI WEBSITE SISTEM INFORMASI PERENCANAAN STUDI STMIK STIKOM INDONESIA DITINJAU DARI PENGGUNA MAHASISWA MENGGUNAKAN TEKNIK WEBQUAL 4.0, FIRSTCLICK, DAN HEURISTIK Dewi, Ni Wayan Jeri Kusuma; Candiasa, I Made; Indrawan, Gede
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol 9, No 2 (2020)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v9i2.23212

Abstract

ABSTRAKSIPERI merupakan singkatan dari Sistem Informasi Perencanaan Studi yang dikembangkan berbasis web yang bertujuan untuk mempermudahkan dalam perencaaan kartu studi mahasiswa, melihat data nilai, serta untuk melihat mata kuliah yang ditawarkan tiap semesternya. Pada kenyataannya SIPERI belum pernah dilakukan evaluasi oleh pengguna. Tujuan dari penelitian ini untuk mengetahui hasil evaluasi pada SIPERI dengan menggunakan teknik FirstClick, WebQual dan Heuristik berdasarkan aspek usability. Dalam aspek usability tersebut yang diukur adalah efisiensi, error dan kepuasan pengguna serta memberikan rekomendasi perbaikan. Hasil penelitian menunjukan bahwa SIPERI STIKI dikatakan sudah efisien dari segi pengguna mahasiswa, karena terdapat 14 dari 15 tugas tidak memiliki perbedaan waktu pengerjaan yang signifikan, namun dilihat dari adanya error atau kesalahan yang dilakukan pengguna mahasiswa saat mengerjakan tugas dapat disimpulkan bahwa terdapat masalah usability pada halaman SIPERI STIKI, oleh karena itu dapat dikatakan belum efektif. Sedangkan dari aspek kepuasan pengguna mahasiswa, responden puas dalam menggunakan SIPERI STIKI, hal ini dapat dilihat dari skor kuesioner sebesar 61,67. Berdasarkan hasil evaluasi tersebut dapat disimpulkan bahwa yang menjadi objek penelitian adalah evalausi pada aspek usability SIPERI STIKI. Untuk rekomedasi perbaikan, permasalahan diperoleh dari pengguna expert yang memahami aspek usability dengan teknik Heuristik. Perbaikan dilakukan pada halaman Dashboard, Halaman Hasil Studi, Halaman Data Diri dengan membuat wireframe. Dengan adanya evaluasi pada SIPERI diharapkan dapat mengetahui tingkat kepuasan pengguna serta untuk meningkatkan performa dari SIPERI.Kata kunci: Aplikasi SIPERI, user experience, firstClick, webqual, heuristik evaluation.ABSTRACT SIPERI STIKI is stand for lesson plan information system which was developed by web to make lesson plan card, observe the grade, and observe the courses easier which were offered in each semester for students. In fact, SIPERI STIKI was never been evaluated by user. The purpose of the research was to know the evaluation result of SIPERI STIKI using FirstClick tehnique, WebQual and Heuristic usability aspect. The measurement of usability aspect was efficiency, error and satisfaction of user, and also provide improvement recommendation. The result of the research showed that SIPERI STIKI was already efficient from student user side, because there were 14 from 15 tasks has no significant different timing in the proccess. However, there were some errors which were done by user while doing the task could be concluded that there was usability problem in SIPERI STIKI’s page, Therefore, it was not eficient yet. Whereas, from satisfaction of user aspect, the respondent was satisfied, the result showed that the score of questioner were 61.67. Based on the evaluation result, the object of the research was evaluation of usability aspect of SIPERI STIKI. For improvement recommendation, the problem was obtained by expert user who master Heuristic technique. The improvements were done in Dashboard page, Study Result page, and Personal Data page. Besides, the researcher was modified the improvement by making wireframe. In conclusion, the reseacher were expected to know the satisfaction of user grade and to know the performance of SIPERI STIKI.  Keywords: SIPERI application, user experience, firstClick, webqual, heuristik evaluation.
Master Stockist Customer Segmentation Using RFM Model and Self-Organizing Maps Algorithm Nirwana, Ni Kadek Ayu; Dewi, Ni Putu Wahyuni; Asana, I Made Dwi Putra; Dewi, Ni Wayan Jeri Kusuma; Astari, Gusti Ayu Shinta Dwi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14112

Abstract

Master Stockist PT SNS 21 Bali struggles to identify member performance based on purchasing behavior because the applicable system only records transactions and stock of goods without providing insight into customers. Customer segmentation can be carried out to identify and understand differences in customer purchasing behavior. Therefore, this study aims to determine customer segmentation using the RFM (Recency, Frequency, Monetary) model and the Self-Organizing Maps (SOM) algorithm. Segmentation development uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach. The RFM model numerically represents customer behavior through three variables, while the Self-Organizing Maps algorithm groups customers into segments with similar characteristics. In this research, the best SOM parameters are 750 iterations, learning rate 0.5, radius 0.5, and grid size 1x3, resulting in 3 clusters with a Silhouette Score of 0.647608 and a Davies-Bouldin Index of 0.536503. Cluster 1 consists of 226 new customers with low RFM values who need encouragement to be more active. Cluster 2, comprising seven members, has low recency, high frequency, and high monetary values, representing loyal customers who need to be retained. Cluster 3 consists of 239 inactive customers with high recency, low frequency, and low monetary values, requiring a reactivation strategy.
Land Suitability Analysis Using the Modified Profile Matching Method Pratistha, Indra; Dewi, Ni Wayan Jeri Kusuma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 2 (2024): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.198

Abstract

The plantation sector plays a significant role in Indonesia's economy, particularly in coffee production. In the province of West Nusa Tenggara (NTB), coffee production experienced annual fluctuations from 2018 to 2021. One of the causes is the lack of public understanding in utilizing land according to its natural potential, leading to decreased productivity and land degradation. Based on discussions with plantation experts from Politeknik LPP Yogyakarta, this study identifies land characteristics divided into qualitative data, such as drainage and soil texture, and quantitative data, including temperature, rainfall, humidity, elevation, effective soil depth, slope, cation exchange capacity (CEC), base saturation, pH H2O, organic carbon (C-organic) content, and nitrogen (N). The application of the modified profile matching method demonstrates its capability in providing recommendations for coffee crop suitability in East Lombok Regency. Data matching tests between land profile values and coffee crop profile values, involving experts from Politeknik LPP Yogyakarta and the NTB Provincial Agriculture Office, resulted in liberica coffee being ranked first in eight sub-districts. However, in one sub-district, Sembalun, robusta coffee did not rank second, as arabica coffee was preferred.
Comparison of random forest and SVM methods in sentiment analysis about electric cars in Indonesia Pratistha, Indra; Iskandar, Adi Panca Saputra; Lanang, Eugenius Gene Rangga; Dewi, Ni Wayan Jeri Kusuma
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.424

Abstract

This study examined public sentiment toward electric vehicles (EVs) in Indonesia, where the adoption of EVs reached 28,188 registered units in 2023. The research analyzed user-generated content from the social media platform X (formerly known as Twitter), collecting 1,507 tweets that underwent preprocessing, including text normalization and sentiment labeling. Two machine learning models, Random Forest and Support Vector Machine (SVM), were implemented to classify the tweets into positive and negative sentiments. Each model was evaluated under three experimental scenarios with varying training dataset sizes. The results indicated that the SVM model achieved the best performance in the third scenario, with an accuracy of 81.3%, precision of 88%, and recall of 91%. In comparison, Random Forest achieved its highest results in the same scenario, with an accuracy of 77%, precision of 91%, and recall of 81%. These findings demonstrated that SVM outperformed Random Forest in terms of overall balance between accuracy and recall, making it the more effective model for sentiment classification in this context.
Pengenalan Coding Untuk Siswa SD Pelangi Jimbaran Dewi, Ni Wayan Jeri Kusuma; Antara, I Gede Made Yudi; Kusuma, Aniek Suryanti
Parta: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/parta.v6i1.6782

Abstract

Keterampilan pemrograman atau coding pada jenjang pendidikan dasar di Indonesia masih belum memperoleh perhatian yang memadai dan belum terintegrasi dalam kurikulum formal, berbeda dengan negara maju yang telah menerapkan pendekatan Computational Thinking sejak dini. Merespons tantangan tersebut, kegiatan Pengabdian kepada Masyarakat (PKM) ini dilaksanakan di SD Pelangi Jimbaran dengan tujuan meningkatkan literasi digital siswa melalui pelatihan bertema “Pengenalan Dampak Teknologi Informasi, Internet, dan Coding” menggunakan pendekatan berbasis aljabar dan bahasa pemrograman Python. Pelatihan diberikan kepada siswa kelas IV hingga VI dengan metode kombinatif berupa ceramah, demonstrasi, dan praktik langsung yang dirancang secara bertahap dan kontekstual. Hasil evaluasi menunjukkan peningkatan signifikan pada rata-rata pemahaman siswa, yaitu dari 43,6 menjadi 75,1 untuk materi TI, dari 45,4 menjadi 77,6 untuk pemanfaatan internet, serta dari 41,7 menjadi 76,4 untuk kemampuan dasar coding. Selain peningkatan kognitif, kegiatan ini juga mendorong kepercayaan diri, rasa ingin tahu, dan partisipasi aktif siswa dalam proses pembelajaran. Temuan ini memperkuat bukti bahwa pendekatan edukatif yang interaktif dan berbasis praktik efektif dalam membentuk pola pikir logis, kritis, dan sistematis sejak usia sekolah dasar. Oleh karena itu, kegiatan ini berkontribusi dalam menyiapkan generasi muda yang adaptif terhadap perkembangan teknologi digital dan mampu menggunakan teknologi secara bijak dan produktif.
Comparison Of the Accuracy of Decision Tree Algorithms C4.5 And C5.0 In Predicting Tuition Payment Delays at Mts. Al-Jabar Bali Dewi, Ni Wayan Jeri Kusuma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

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

Abstract

Delays in the payment of Educational Development Contributions (SPP) have become a major issue impacting financial management at MTs. Al-Jabar Bali, with approximately 60% of students experiencing payment delays each year. This study aims to compare the accuracy of Decision Tree algorithms C4.5 and C5.0 in predicting SPP payment delays. The research method adopts the CRISP-DM approach and is implemented using Python on the Google Colaboratory platform. The data used includes students’ payment histories, parents' occupations, and income. The models were evaluated using Accuracy, Precision, and Recall metrics. The results show that the C5.0 algorithm has higher accuracy (98%) compared to C4.5 (89%). The C5.0 algorithm is recommended as an effective predictive model to assist schools in making strategic financial management decisions.
Co-Authors Aniek Suryanti Kusuma, Aniek Suryanti Asana, I Made Dwi Putra Astari, Gusti Ayu Shinta Dwi Dewi, Ni Putu Wahyuni Gede Indrawan I Gede Made Yudi Antara I Made Candiasa Indra Pratistha Iskandar, Adi Panca Saputra Lanang, Eugenius Gene Rangga Nirwana, Ni Kadek Ayu