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Pemilihan Aplikasi Meeting Online Untuk Mendukung Work From Home Menggunakan Metode AHP Danang, Deodatus; Mustika, Wida Prima; Merdekawati, Agustiena
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v4i2.245

The Indonesia government has started to prepare the strategies by setting the new policies regarding the spread of the coronavirus (Covid-19) since the first case was found on March, 2nd 2020. One of those policies is appealing to all government staffs/State C3il Apparatus and pr3ate employees to apply the system of remote working (Work from Home). To support the act3ity of remote working, the employees have been using some applications for online meetings to communicate with colleagues, superiors, and business partners in resolving the workloads without direct physical contacts. These applications needed are Zoom, Google Meet, Skype, and Webex. The writer conducted a study using the Analytical Hierarchy Process (AHP) method to determine the selection of the appropriate application for online meetings according to the priority criteria required by the employees who work at home. AHP method can provide the best order that will produce criteria and alternat3es with the highest values. The research was conducted based on the criteria for application features, ease of use, number of participants, duration of time, bandwidth requirements, and application security. The result for alternat3es with priority results was the Zoom application with the highest value of 0.341 (34.1%), followed by Google Meet with a value of 0.319 (31.9%), Skype with a value of 0.187 (18.7%), and Webex. With a value of 0.153 (15.3%) as the lowest order.

Perancangan Sistem Pendukung Keputusan Rekomendasi Penentuaan Penerimaan Beasiswa Merdekawati, Agustiena; Azlina, Yunidyawati; Wulansari, Murwani
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.516

The scholarship acceptance process at one of the universities in Jakarta is based on several criteria, including the distance from the residence to the campus, active organizations, participating in UKM, GPA, having parents or guardians, parents' or guardians' jobs, income level, number of family dependents, ownership of a residence. In 2020, there were 1000 students who registered with various majors, so the scholarship selection team had difficulty in determining the scholarship acceptance selection quickly and accurately. In addition, subjective selection determinations were still found so that they were not on target and caused errors in determining policies to increase. Recording and determining scholarship acceptance were still done manually, namely via Excel. By entering detailed data on scholarship applicants and then calculating the value of each criterion met by scholarship applicants, this process is very complicated in processing scholarship determination. With this problem, research was carried out with data mining, using a PSO-based decision tree algorithm model with a PSO-based naïve bayes algorithm. By comparing the accuracy results of the two models, the highest accuracy was obtained with the PSO-based decision tree algorithm model. Furthermore, designing a web-based Decision support system for scholarship selection.

Pemilihan Aplikasi Meeting Online Untuk Mendukung Work From Home Menggunakan Metode AHP Danang, Deodatus; Mustika, Wida Prima; Merdekawati, Agustiena
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

The Indonesia government has started to prepare the strategies by setting the new policies regarding the spread of the coronavirus (Covid-19) since the first case was found on March, 2nd 2020. One of those policies is appealing to all government staffs/State C3il Apparatus and pr3ate employees to apply the system of remote working (Work from Home). To support the act3ity of remote working, the employees have been using some applications for online meetings to communicate with colleagues, superiors, and business partners in resolving the workloads without direct physical contacts. These applications needed are Zoom, Google Meet, Skype, and Webex. The writer conducted a study using the Analytical Hierarchy Process (AHP) method to determine the selection of the appropriate application for online meetings according to the priority criteria required by the employees who work at home. AHP method can provide the best order that will produce criteria and alternat3es with the highest values. The research was conducted based on the criteria for application features, ease of use, number of participants, duration of time, bandwidth requirements, and application security. The result for alternat3es with priority results was the Zoom application with the highest value of 0.341 (34.1%), followed by Google Meet with a value of 0.319 (31.9%), Skype with a value of 0.187 (18.7%), and Webex. With a value of 0.153 (15.3%) as the lowest order.

CLASSIFICATION OF PRODUCT PREDICATES BASED ON SALES RATE USING THE C4.5 DECISION TREE ALGORITHM IN RETAIL COMPANIES Haafizh, Salman; Merdekawati, Agustiena; Yuliani, Yuri
International Journal Multidisciplinary (IJMI) Vol. 1 No. 3 (2024): International Journal Multidisciplinary (IJMI)
Publisher : Antis-Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijmi.v1i3.201

General Background: Retail companies serve as crucial intermediaries between producers and end consumers, providing a wide range of products to meet daily needs. Specific Background: However, many retail companies encounter challenges in inventory management and stock provision, often stemming from insufficient analysis of sales and inventory data. Knowledge Gap: Existing research on inventory management in retail lacks a focus on predictive analytics techniques that leverage sales data to optimize ordering strategies. Aims: This study aims to identify sales patterns using the Decision Tree C4.5 algorithm, with the goal of predicting sales for various products to enhance ordering strategies. Results: Employing primary data collected via the company’s API and direct interviews with Order Management staff and the regional director of Jabodetabek, sales data spanning six months (November 2023 to April 2024) was analyzed using data mining techniques on the RapidMiner platform. The findings reveal that the Decision Tree algorithm effectively identifies product sales predicates, achieving a model accuracy of 96.20%. Novelty: This research introduces a data-driven approach to inventory management in retail, utilizing advanced decision tree algorithms for enhanced sales prediction. Implications: The implementation of the proposed model is expected to significantly improve the efficiency and effectiveness of the company’s ordering processes, ultimately leading to better inventory control and customer satisfaction.

PENGARUH KUALITAS APLIKASI MOBILE, KEPERCAYAAN, HARGA, DAN PROMOSI TERHADAP KEPUASAN KONSUMEN PADA ERA PANDEMI COVID 19 (Studi Kasus Pada Online Shop: Tokopedia, Shopee, Bukalapak) Merdekawati, Agustiena; Perangin-angin, Elvi Sunita; Masshitah, Sari
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 6 No. 3 (2021)
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

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

Penelitian ini bertujuan untuk menganalisis pengaruh kualitas aplikasi mobile, kepercayaan, harga, dan promosi terhadap kepuasan konsumen pada Tokopedia, Shopee, dan Bukalapak secara parsial dan simultan. Penelitian ini menggunakan teknik kuantitatif dengan ciri penelitian adalah deskriptif. Sampel penelitian ini sebanyak 68 responden dengan purposive sampling sebagai teknik pengambilan sampelnya. Data diperoleh dengan cara menyebar kuesioner menggunakan google form ke beberapa media sosial. Dalam penelitian ini menggunakan metode analisis yaitu regresi linier berganda. Hasil dari penelitian ini yaitu pada tokopedia, variabel kualitas mobile, kepercayaan, harga, dan promosi tidak ada pengaruh terhadap kepuasan konsumen. Pada regresi linear berganda, jika semua variabel, seperti variabel kualitas mobile, kepercayaan, harga dan promosi diberikan nilai 0, maka kepuasan konsumen yang paling besar adalah Tokopedia sebesar 4,014, selanjutnya yang kedua Shopee sebesar 3,572, dan ke tiga Bukalapak sebesar 3,16.

Komparasi Dua Metode Algoritma Klasifikasi Untuk Prediksi Pemberian Kartu Jakarta Pintar Merdekawati, Agustiena; Kumalasari, Jefina Tri
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 2 (2022): InfoTekJar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v6i2.4891

Mendapatkan program Kartu Jakarta Pintar (KJP) harus memenuhi berbagai persyaratan dan kriteria, sayangnya proses penerimaan ini masih dilakukan secara subjektif sehingga rentan untuk tepat sasaran. Knowledge Discovery in Database (KDD) diperlukan dalam penentuan penerima KJP dengan menemukan pola prediksi terbaik. Penelitian ini membandingkan algoritma klasifikasi yaitu ID3 dan Naïve Bayes guna mengekstrak data kemudian mencari model yang sesuai dalam penentuan proses penerimaan KJP dengan menggunakan sekelompok data sehingga didapatkan persentase precision, recall dan accuracy. Keduanya memiliki proses awal yang sama yaitu pre processing atau data cleaning guna membuang data yang tidak sesuai baik data kosong maupun tidak konsisten. Pada algoritma ID3 digunakan pohon keputusan dimana sebelumnya diperlukan pencarian entropi dan gain sedangkan pada Naïve Bayes dengan menghitung jumlah class. Hasil Analisa keduanya kemudian dilakukan proses pengujian dilakukan untuk membandingkan tingkat akurasi data dengan menerapkan confussion matrix dan visualisasi kurva ROC. Hasil pengujian didapat algoritma ID3 menunjukkan tingkat akurasi yang lebih tinggi 77,78% setelah dibandingkan dengan Naïve Bayes. Tools yang digunakan dalam pengolahan data ini yaitu Rapid Miner.

KEPUASAN MAHASISWA TERHADAP MUTU LAYANAN PENDIDIKAN UNIVERSITAS BINA SARANA INFORMATIKA Merdekawati, Agustiena; Azlina, Yunidyawati; Wulansari, Murwani
ECOBISMA (JURNAL EKONOMI, BISNIS DAN MANAJEMEN) Vol 11, No 1 (2024): ECOBISMA
Publisher : Published by the Faculty of Economics and Business, University of Labuhanbatu, North Sumat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/ecobi.v11i1.5272

The purpose of this study was to determine how much influence student satisfaction has on the quality of education services, to determine the student satisfaction index on the quality of education services, and to find out which services still have to be improved, paid attention to and maintained. The population in this study are students of Bina Sarana Informatika University and sampling using probability sampling with simple random sampling method. physical evidence, reliability, and assurance variables have a positive and significant effect on the quality of education services. While partially the variables of empathy and responsiveness do not have a positive and significant effect on the quality of educational services. Based on the results of the CSI score, it produces a score of 78%, meaning that students are satisfied with the service. Results with the IPA method, there are still several statements that must be improved. These results cannot be separated from the role of lecturers, administrative staff and sections, facilities and others who are in the Bina Sarana Informatika University environment.

Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Wilayah pada Pelanggaran Lalu Lintas Agustiena Merdekawati; Jefina Tri Kumalasari
SATIN - Sains dan Teknologi Informasi Vol 10 No 1 (2024): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v10i1.1111

Dengan banyaknya jumlah kendaraan yang terus meningkat dapat bertambahnya angka tingkat kemacetan. Semakin meningkatnya kemacetan suatu wilayah maka dapat menyebabkan peningkatan pelanggaran, seperti pelanggaran rambu lalu lintas, tidak memakai helm, surat berkendara yang tidak lengkap, melawan arah, dan lainnya. Berbagai cara untuk menertibkan lalu lintas terus dilakukan kepolisian dan dinas perhubungan agar terciptanya kenyamanan dan keselamatan lalu lintas. Tujuan dari penelitian ini yaitu untuk menentukan pola pengelompokan data pelanggaran lalu lintas, memudahkan untuk analisa wilayah mana yang memiliki pelanggaran paling banyak sehingga dapat membuat kebijakan atau aturan untuk mengurangi pelanggaran dan tingkat kecelakaan pun menjadi menurun. Dengan menggunakan tahap Knowledge Discovery in Database (KDD) dalam konsep data mining sebagai metode penelitian. Penelitian ini membandingkan metode algoritma k-means dengan algoritma k-medoids, dengan pengukuran performance menggunakan Dalvies Bouldin Index (DBI). Hasil dari penelitian ini, memiliki kesamaan kelompok cluster yaitu dua, dengan cluster 0 wilayah pelanggaran lalu lintas Menteng, Sawah Besar, Senen, Tanah Abang, Cempaka Putih, Gambir, Johar Baru, dan Pal Merah, serta cluster 1 dengan wilayah Kemayoran. Hasil performance dari kedu metode tersebut, pada model kmedoids memiliki hasil lebih besar disbanding model k-means, sehingga model k-means memiliki hasil yang mendekati 0, yaitu sebesar -0,197

Komparasi Algoritma Data Mining Sebagai Prediksi Harapan Hidup Pasien Gagal Jantung: Komparasi Algoritma Data Mining Sebagai Prediksi Harapan Hidup Pasien Gagal Jantung Merdekawati, Agustiena
CSRID (Computer Science Research and Its Development Journal) Vol. 14 No. 3: October 2022
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.14.3.2022.188-202

The heart is the most vital organ of the body. Heart failure is the leading cause of death with the largest number of cases. Therefore, it is necessary to estimate the biggest factor in life expectancy in patients with heart failure, so as to reduce mortality. In predicting the life expectancy of heart failure by using Knowledge Discovery in Database (KDD) it is possible to find predictive patterns of life expectancy for heart failure, so that it can reduce mortality. In this study using the C4.5 algorithm and the C4.5 algorithm with PSO (Particle Swarm Optimization) to obtain a predictive pattern of life expectancy for heart failure which then obtained the percentage of precision, recall and accuracy. This research is to produce a predictive pattern of life expectancy for heart failure with the criteria for the length of time the action has a top priority. By using the C4.5 algorithm, an accuracy of 73.33% is obtained, while using the C4.5 and PSO algorithms an accuracy of 99.00% is obtained, so it can be concluded based on the accuracy level that the C4.5 and PSO algorithm modeling has a higher accuracy than the C4.5 algorithm. . By using the C4.5 algorithm, the ROC graph accuracy is 0.897%, while using the C4.5 and PSO algorithms the ROC graph accuracy is 1.00%, so it can be concluded based on the ROC graph accuracy level that the C4.5 and PSO algorithm modeling has more accuracy. higher than the C4.5 algorithm.

KLASTERISASI PELANGGAN BERDASARKAN PERILAKU KONSUMEN MENGUNAKAN K-MEANS Kumalasari, Jefina Tri; Merdekawati, Agustiena
Journal of Information System, Applied, Management, Accounting and Research Vol 9 No 4 (2025): JISAMAR (Journal of Information System, Applied, Management, Accounting and Resea
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v9i4.2124

Mengelola loyalitas pelanggan dan menjangkau pembeli merupakan tantangan terbesar yang dihadapi industri retail. Segmentasi atau pengelompokan pelanggan merupakan strategi yang dilakukan dengan memisahkan pelanggan ke dalam beberapa kelompok berdasarkan perbedaan karakteristik, perilaku, maupun kebutuhan mereka. Pembagian ini bertujuan membantu pebisnis guna memenuhi kebutuhan mereka dengan mengoptimalkan layanan dan produk. Metode Clustering digunakan untuk mengidentifikasi beberapa segmen pelanggan. Berdasarkan hasil pengujian maka terlihat pada jumlah cluster optimal adalah K = 3 karena memiliki nilai tertinggi yaitu 0.33861316, hal ini didukung setalah dilakukan pengujian elbow methode. Penerapan Algoritma K-Means dalam mengkluster perilaku pelanggan memberikan wawasan mengenai variasi pola pembelian di antara pelanggan. Hasil ini dapat digunakan untuk mengembangkan strategi bisnis yang lebih terarah dan efektif. Namun, kondisi ekonomi dan faktor gaya hidup turut memberi pengaruh besar sehingga pola pengeluaran dalam populasi sangat bervariasi.

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