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All Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Jurnal Inovatif Wira Wacana JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Liliawati, Swat Lie
Universitas Kristen Maranatha
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences

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PELATIHAN COMPUTATIONAL THINKING UNTUK GURU SDK 6 BPK PENABUR BANDUNG MELALUI BEBRAS TASK DAN AKTIVITAS UNPLUGGED Mewati Ayub; Hendra Bunyamin; Oscar Karnalim; Robby Tan; Maresha Caroline Wijanto; Doro Edi; Julianti Kasih; Andreas Widjaja; Adelia; Meliana Christianti; Wenny Franciska Senjaya; Swat Lie Liliawati; Rossevine Artha Nathasya
Jurnal Abdimas Ilmiah Citra Bakti Vol. 5 No. 3 (2024)
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jailcb.v5i3.3799

Abstract

Konsep computational thinking (CT) diperlukan dalam dunia digital saat ini agar setiap orang dapat belajar dan bekerja secara cerdas. Untuk membangun kembali interaksi antar guru dan siswa yang terkendala pada saat pandemi Covid 19, maka interaksi yang efektif antar guru dan siswa dalam pembelajaran pasca pandemi dapat dilakukan dengan menerapkan CT dalam pembelajaran.  Pelatihan CT untuk guru-guru SDK 6 BPK Penabur dilakukan dengan tujuan agar setiap guru dapat menerapkan konsep CT dan aktivitas unplugged dalam pembelajaran yang bersifat interaktif. Pelatihan guru yang dilaksanakan secara luring pada 15 Maret 2024 dan 22 Maret 2024, diikuti oleh 30 orang peserta. Setelah materi konsep CT, Bebras task, dan aktivitas unplugged disampaikan, guru diberi tugas kelompok untuk membuat rencana penerapan CT dalam mata pelajaran serta membuat rencana aktivitas unplugged untuk membantu siswa dalam menerapkan CT dalam persoalan sehari-hari. Hasil dari tugas kelompok yang dibuat peserta menunjukkan nilai rata-rata sangat baik dalam penerapan CT dan aktivitas unplugged. Sebagian besar peserta berpendapat penerapan CT sangat bermanfaat untuk diterapkan dalam pembelajaran di tingkat sekolah dasar untuk melatih anak berpikir kritis dan kreatif dalam memecahkan masalah di kehidupan sehari-hari.
Ekstraksi Perilaku Pasien Pada Kunjungan Poliklinik Rumah Sakit Menggunakan FP-Growth Liliawati, Swat Lie; Toba, Hapnes; Ayub, Mewati; Mu’min, Aziz; Valentina, Ivana; Metayani, Vanessa; Nava, Vardina
Jurnal Inovatif Vol. 2 No. 3 (2023): Desember 2023
Publisher : Universitas Kristen Wira Wacana Sumba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58300/inovatif.v2i3.681

Abstract

Penerapan sistem informasi management rumah sakit (SIMRS) pada sebuah rumah sakit dapat memberikan pengetahuan baru dalam melakukan pengelolaan rumah sakit dan memungkinkan manajemen rumah sakit untuk memperoleh data pasien dalam jumlah besar mengenai kunjungan pasien. Salah satu tantangan dalam menggunakan big data di rumah sakit adalah ekstraksi perilaku pasien dalam melakukan kunjungan ke poliklinik di rumah sakit. Perilaku kunjungan pasien ini merupakan faktor yang sangat penting bagi pihak management rumah sakit untuk mengambil keputusan yang tepat. Dalam penelitian ini menggunakan metode association rules untuk mengekstrak data kunjungan pasien agar dapat menghasilkan informasi yang baik dan dapat dipahami perilaku kunjungan pasien di rumah sakit. Hasil penelitian ini menunjukan bahwa dengan metode association rules dapat mengekstraksi data kunjungan pasien dan menghasilkan aturan asosiasi yang kuat pada perilaku kunjungan pasien.
Manajemen Risiko Pemasangan Wifi pada Perusahaan Telekomunikasi dengan Framework Risk Information Technology Loudry Palmarums Mustamu; Mewati Ayub; Swat Lie Liliawati
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4491

Abstract

 In this research, an analysis of Wi-Fi products was carried out using Risk Framework, Information Technology (IT) Domain Risk Response, and decision tree method to determine decision making at the Telecommunication companies which occurs from January to September 2021. The Wi-Fi installation process was carried out to assess how far Telecommunication company had responded to problems related to IT risks. This analysis is carried out to help the Telecommunication company create a framework to respond to IT risks that have occurred, such as human risk errors, system disturbances, interference fromoutside parties, inventory control, as well as responding to problems related to IT risks such as problems related to possible risks to the system used. The goal is to provide recommendations to the company in accordance with the IT Risk Framework. Thedata sources are derived from a direct interview with the manager of the Telecommunication company and customer service data. The analysis refers to the process of installing Wi-Fi for the customers. Customer service data is analyzed using the Decision Tree in Weka. The results of the analysis are expected to support the Telecommunication company to be better inresponding and reacting to IT risk and incidents that have occurred, those that may occur at telecommunication installation.
Analisis Deret Waktu dari Produk yang Terjual Menggunakan Beberapa Teknik Populer Laras Ervintyana; Andreas Widjaja; Swat Lie Liliawati
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 1 (2023): JuTISI (in progress)
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i1.5933

Abstract

Sales is the most important component in and industrial company. This is because, income comes into the company if sales is running, therefore it is important to do an analysis of the products sold so that the company can prepare earlier before the demand for these products come, in order to produce better income. This study uses ARIMA, SVR, FFT and Prophet to forecast and with MAPE and RMSPE as the measure level of accuracy, also to see if there is any seasonality in the product that is being analyzed, seasonal_decompose is used. The results of the analysis show that ARIMA and Prophet are the best forecasting methods, this is because both methods have the lowest MAPE and RMSPE value. After being analyzed using seasonal_decompose, it was found that all of the products studied have a pattern that repeats itself at a certain time every quarter. For more further analysis, it was done by head-to-head comparison, where 20 product samples of each category was used. By this analysis it was clear that products in Category 1 are better to use ARIMA and products in Category 2 are better to use Prophet.
Pengembangan Computational Thinking Siswa melalui Tantangan Bebras 2023 di Biro Bebras Universitas Kristen Maranatha Ayub, Mewati; Tan, Robby; Wijanto, Maresha Caroline; Nathasya, Rossevine Artha; Adelia, Adelia; Senjaya, Wenny Franciska; Karnalim, Oscar; Surjawan, Daniel Jahja; Edi, Doro; Toba, Hapnes; Christianti, Meliana; Kasih, Julianti; Risal, Risal; Yulianti, Diana Trivena; Zakaria, Teddy Marcus; Liliawati, Swat Lie
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 15, No 3 (2024): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v15i3.18162

Abstract

Pengabdian masyarakat yang dilakukan bertujuan untuk mengembangkan kemampuan Computational Thinking (CT) siswa melalui kegiatan Tantangan Bebras. Tantangan Bebras adalah kegiatan untuk memberi tantangan kepada siswa berupa sekumpulan Bebras task yang harus diselesaikan dalam waktu terbatas. Bebras task mengandung konsep Computational Thinking dan informatika yang dikemas dalam bentuk persoalan yang harus dipecahkan. Tantangan Bebras diadakan oleh Bebras Indonesia setiap tahun pada minggu kedua bulan November dengan melibatkan mitra Biro Bebras di seluruh Indonesia. Biro Bebras Universitas Kristen Maranatha mempersiapkan guru pendamping siswa melalui pelatihan guru agar dapat membimbing siswa dalam berlatih memecahkan Bebras task. Dalam pelatihan, guru diperkenalkan dengan Bebras task melalui kuis yang kemudian dibahas bersama. Guru juga diberi materi pengenalan CT dan aktivitas unplugged. Masa pendaftaran peserta Tantangan Bebras dilakukan setelah pelatihan, pendaftaran dilakukan secara kolektif melalui sekolah. Ada 4 kategori lomba, yaitu SiKecil untuk SD kelas 1-3, Siaga untuk SD kelas 4-6, Penggalang untuk SMP, dan Penegak untuk SMA. Terdapat 54 sekolah yang mendaftarkan siswanya. Menjelang hari Tantangan diadakan technical meeting untuk guru sebagai persiapan untuk mendampingi siswa pada saat uji coba akun dan pada saat tantangan. Peserta yang mengikuti Tantangan melalui Biro Bebras UK Maranatha berjumlah 3429 orang, yang terbanyak adalah kategori Penggalang. Hasil Tantangan menunjukkan kategori Siaga dan SiKecil sudah baik, sedangkan kategori Penggalang dan Penegak perlu mempersiapkan diri lebih baik di tahun mendatang.
Evaluasi Hasil Neural Style Transfer Berbagai Gambar Pola Menggunakan Feature Similarity Index Metayani, Vanessa; Liliawati, Swat Lie; Widjaja, Andreas
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 2 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i2.9380

Abstract

This research was conducted by applying the Neural Style Transfer method to various sets of content and style images, and then calculating the FSIM value for each pair of result and original images. Analysis was done on factors such as art style complexity, resolution and other special characteristics such as colour and texture that can affect the FSIM value. The purpose of this research is to identify whether there are factors that affect FSIM performance such as art style complexity, resolution, or other special characteristics such as colour and texture. This research is expected to be able to help artists who want to change the art style with Neural Style Transfer but still maintain the originality of the image and still be recognised by evaluating the results using FSIM and help artists to develop and produce artistic digital artworks with good quality. The results show that varying FSIM values can depend on the complexity of the art style and image resolution. Simple art styles and high-resolution images tend to produce higher FSIM values, indicating that the image structure is easily preserved. As long as the resolution and colours or textures do not change the main structure, the FSIM results will not decrease significantly. To support the research analysis, the Analysis of Variance (ANOVA) statistical test was used to measure the significance of the effect of complexity and resolution on FSIM and the Cronbach’s Alpha test to test the reliability of the general public and expert surveys. Based on the ANOVA statistical test results, there was insufficient evidence to reject the null hypothesis, so complexity and resolution did not have a significant influence on FSIM. From the Cronbach’s Alpha test results, the public assessment survey received a result of 0.94 and 0.91 for the expert assessment survey. These results indicate that the results from the surveys are reliable as subjective data for the research.
Deteksi dan Klasifikasi Tingkat Keparahan Jerawat: Perbandingan Metode You Only Look Once Veby Agustin, Giezka; Ayub, Mewati; Liliawati, Swat Lie
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.9414

Abstract

Acne (Acne vulgaris) is one of the most common skin diseases, especially on the face. Accurate diagnosis and proper treatment are important for optimal care results and improving the accuracy of detection and classification of acne severity. YOLO (You Only Look Once) is a deep learning method used for object detection in images. This study compares the results and performance of YOLOv5 and YOLOv8 in detecting acne on the face. Several experiments were also conducted with data pre-processing, model size, and the use of different basic hyperparameters on both models to understand the impact and differences between YOLOv5 and YOLOv8. The results show that YOLOv5 overall has higher performance in detecting acne compared to YOLOv8, which requires larger hyperparameter values and model sizes to achieve the most optimal results. Conservative hyperparameters (with relatively smaller values or sizes) on YOLOv5 contribute to better performance.
Co-Authors Adelia Adelia, Adelia Andreas Widjaja Andreas Widjaja Daniel Jahja Surjawan Diana Trivena Yulianti Doro Edi Hapnes Toba Haryadi, Lucky Surya Hendra Bunyamin Johan, Meliana Christianti Julianti Kasih Julianti Kasih, Julianti Laras Ervintyana Loudry Palmarums Mustamu Maresha Caroline Wijanto Maresha Caroline Wijanto Metayani, Vanessa Mewati Ayub Mu’min, Aziz Mustamu, Loudry Palmarums Nava, Vardina Oscar Karnalim, Oscar Robby Tan Rossevine Artha Nathasya Teddy Marcus Zakaria Valentina, Ivana Veby Agustin, Giezka Wenny Franciska Senjaya Wibisono, Christian Widyaya, Juan Elisha