Article Per Year (5 Year)
p-Index From 2019 - 2024
0.61
P-Index
This Author published in this journals
All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA)
Asih, Munjiat Setiani
Unknown Affiliation
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Prediksi Tingkat Penggunaan Air Minum Oleh Konsumen di Depot Monica Water Menggunakan Metode Weighted Moving Average Qardhafi, Rayan; Faisal, Ilham; Sundari, Siti; Asih, Munjiat Setiani
TIN: Terapan Informatika Nusantara Vol 2 No 3 (2021): Agustus 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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

Abstract

The use of existing data to support activities in decision making is not enough just to rely on operational data only, we need a data analysis to explore the potential of existing information. One of the data mining techniques that can be done is prediction. Prediction is needed to determine when an event will occur or arise, so that appropriate action can be taken. Prediction is not always 100% right, but with the right method selection can make predictions with a small error rate. Monica Water Depot is a business engaged in Refill Drinking Water. To find out the level of consumer use of refill drinking water, the business owner must know some predictions of upcoming purchases so that the business owner can provide gifts that are in accordance with the predicted purchases. Predictions on the level of consumer use can help employers provide gifts that are in accordance with consumer purchases, by knowing the purchase in the next period, the business owner can find out what gifts will be given to customers. The results of the analysis using I test data, II test data, and III test data obtained by the weight of moving avergae recommended using a weight of 2 months because it has the smallest error value when compared to using 3 months weight. Predictive results can be a recommendation for improvements made by management and as a means of determining business strategies in the future
Klasifikasi Jenis Kendaraan Pada Jalan Raya Menggunakan Metode Convolutional Neural Networks (CNN) Hafifah, Febri; Rahman, Sayuti; Asih, Munjiat Setiani
TIN: Terapan Informatika Nusantara Vol 2 No 5 (2021): Oktober 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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

Abstract

Traffic congestion is a major problem that occurs in big cities in Indonesia. This can cause various negative impacts such as waste of fuel, waste of time, and air pollution. Therefore, the government divides the types of highways and only allows large cargo trucks to pass on arterial roads. So it is necessary to have a smart city to implement government policies in order to overcome the impacts caused by traffic congestion. Classification of the types of vehicles that pass on the highway needs to be done so that there are no vehicle violations outside the specifications that are allowed to enter certain highways. Classification of vehicle types using the Convolution Neural Network (CNN) method. The architecture used is in the form of an existing CNN architecture or an existing CNN architecture, namely googlenet and shufflenet. We fine tune Googlenet and Shufflenet to get maximum accuracy. The dataset used is data taken from several CCTV camera points in several cities in Indonesia in July 2021. The proposed method can classify vehicle types with an accuracy of 95.88% Googlenet and 96.48% Shufflenet. Thus, it is hoped that it can contribute to researchers to develop a better CNN so that it can be implemented for the benefit of road traffic in Indonesia in the future
Analisis Prediksi Jumlah Mahasiswa Universitas Harapan Medan Menggunakan Metode Least Square Asih, Munjiat Setiani; Hasibuan, Ade Zulkarnain
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol 5 No 2 (2023): Edisi Oktober
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jikstra.v5i2.879

Abstract

Peningkatan jumlah mahasiswa di perguruan tinggi menjadi fokus utama bagi pengelola universitas dalam merencanakan strategi pengembangan dan pelayanan pendidikan yang lebih baik. Memprediksi jumlah mahasiswa di masa depan menjadi penting dalam konteks perencanaan jangka panjang dan pengambilan keputusan strategis di lembaga pendidikan tinggi. Penelitian ini bertujuan untuk memprediksi jumlah mahasiswa Universitas Harapan Medan pada tahun 2024 berdasarkan data historis dari tahun 2017 hingga tahun 2023 menggunakan metode least square. Metode prediksi least square digunakan untuk menyesuaikan model regresi linear yang menghubungkan variabel independen (tahun) dengan variabel dependen (jumlah mahasiswa). Data historis tentang jumlah mahasiswa selama periode waktu yang ditentukan digunakan untuk mengembangkan model prediksi yang akurat. Hasil penelitian menunjukkan bahwa berdasarkan model least square yang disesuaikan dengan data historis, kenaikan jumlah mahasiswa Universitas Harapan Medan diproyeksikan sebesar 14,17% pada tahun 2024. Hasil prediksi ini memberikan wawasan yang berharga bagi pengelola universitas dalam merencanakan dan mengimplementasikan strategi promosi, alokasi sumber daya, dan rencana penerimaan mahasiswa baru. Dengan pemahaman yang lebih baik tentang tren pertumbuhan jumlah mahasiswa, universitas dapat meningkatkan efisiensi operasional, mengoptimalkan pelayanan akademik dan administratif, serta memperkuat posisinya di pasar pendidikan tinggi. Penelitian ini memiliki implikasi penting bagi manajemen pendidikan tinggi dalam konteks perencanaan strategis dan pengembangan lembaga pendidikan.
Co-Authors Faisal, Ilham Hafifah, Febri Hasibuan, Ade Zulkarnain Qardhafi, Rayan Rahman, Sayuti Siti Sundari