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All Journal Jurnal Transformatika Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK) Indonesian Journal of Business Intelligence (IJUBI) Djtechno: Jurnal Teknologi Informasi Jurnal Pengabdian Masyarakat Nusantara International Journal of Management Science and Information Technology (IJMSIT) Jurnal Cakrawala Informasi (JCI) Jurnal Suara Pengabdian 45 Jurnal Manajerial dan Bisnis Tanjungpinang
Mutiarachim, Atika
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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OPTIMALISASI PREDIKSI SAHAM APPLE DAN SAMSUNG DENGAN ALGORITMA BPNN Mutiarachim, Atika; Kusumawati, Yupie; Nurchayati, Nurchayati; Indriawati, Aulia
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.6707

Abstract

Pasar saham global khususnya bidang teknologi mengalami volatilitas yang signifikan, dengan saham Apple Inc. dan Samsung Electronics Co., Ltd sebagai pemain utama. Prediksi signifikan sangat diperlukan untuk mengurangi resiko investasi. Penelitian ini menganalisis dan membandingkan kinerja Backpropagation Neural Network (BPNN) dalam memprediksi pergerakan saham Apple dan Samsung. Dataset publik diperoleh dari Kaggle, saham Samsung dengan 6128 data periode 4 Januari 2000 sampai 13 Juni 2024 dan saham Apple dengan 2476 data periode 2 Januari 2014 sampai 31 Oktober 2023. Metode BPNN diterapkan dengan optimasi parameter learning rate, momentum, dan training cycle, pembagian data 10-fold cross validation, evaluasi nilai Root Mean Square Error (RMSE). Hasil terbaik menunjukkan konfiguasi optimal diperoleh dari learning rate 0.1, momentum 0.9, error epsilon 1.0E-4 dan training cycle 60. Nilai RMSE terbaik saham Apple 0.802 0.263 dengan akurasi 99.85%, dan pada saham Samsung RMSE terbaik 399.806 102.670 dengan akurasi 99.36%. Penelitian membuktikan BPNN dengan pola 0.1-0.9-60 sangat efektif memprediksi harga Close sehingga mampu memberikan kontribusi signifikan bagi investor dalam melakukan evaluasi investasi sebagai strategi meminimalisir resiko saham.
The Role of Driver Services and Application Quality in Enhancing Gojek Customer Loyalty Through Satisfaction Mutiarachim, Atika; Yuniarti, Nur Atika
SIMAK Vol. 22 No. 02 (2024): Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK)
Publisher : Faculty of Economics dan Business, Atma Jaya Makassar University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35129/simak.v22i02.562

Abstract

The purpose of this research is to determine the influence of Driver Services and Application Quality on Customer Loyalty through Customer Satisfaction, both directly and indirectly. Primary data was obtained through a Google Form questionnaire, the link of which was distributed online to Gojek customers throughout Indonesia resulting 120 respondents. Four data contained missing value and outliers so 116 data were used. Data was processed using Smart PLS 4.0. This research propose seven hypotheses. The results show five hypotheses can be accepted, however hypothesis of Application Quality on Customer Loyalty and Driver Services on Customer Loyalty through Customer Satisfaction obtained p-value > 0.05 so concludes to not influential.
Optimasi Prediksi Pemasaran Nasabah Deposito Bank dengan Metode Klasifikasi Logistic Regression Mutiarachim, Atika; Jaluanto Sunu Punjul Tyoso
Jurnal Cakrawala Informasi Vol 4 No 1 (2024): Juni : Jurnal Cakrawala Informasi
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) - Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jci.v4i1.390

Abstract

The study aims to determine the impact of the Logistic Regression method on the classification of customer bank deposits, using a public UCI Bank Marketing dataset, which contains customer-specific information of bank deposit telemarketing activities. Data has a binomial label consisting of 'yes' for subscribers and 'no' for non-subscribers. The data preprocessing phase is done with downsampling to make the amount of data more symmetrical, then data selection and data transformation to ensure that the data used values are consistent, attribute selection to select the attributes most accurately used and give significant influence. Classification is done using the Logistic Regression algorithm. Data is shared using a split method with 90% training data and 10% testing data, with the aim of optimizing the training process. The performance result consists of an accuracy of 88.53%, a classification error value of 11.4%, can be categorized as low, showing only a few errors produced by the algorithm model, a kappa value of 0.68 close to 1, so it is categorized well, a low RMSE rating of 0.3 indicates a model accurate, and a high AUC percentage of 93.4% indicates the correct algority used in this dataset, because it produces a good performance value.
Co-Authors Aditya Putra Ramdani Basirudin Ansor Dhendra Marutho Fari Katul Fikriah Indriawati, Aulia Jaluanto Sunu Punjul Tyoso Jaluanto Sunu Punjul Tyoso Kusumawati, Yupie Nurchayati Nurchayati Pamudya, Ryan Arya Puji Setya Sunarka Ryan Arya Pramudya, Ryan Arya Yuniarti, Nur Atika