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All Journal CogITo Smart Journal Jurnal Multidisiplin Madani (MUDIMA) Klabat Journal of Management
Lidya Chitra Laoh
Jurusan Sistem Informasi, Fakultas Ilmu Komputer, Universitas Klabat
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Social Sciences

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Journal : cogito smart journal

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Dividend Payout Forecast : Multiple Linear Regression vs Genetic Algorithm-Neural Network Lidya Chitra Laoh
CogITo Smart Journal Vol 5, No 2 (2019): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.849 KB) | DOI: 10.31154/cogito.v5i2.210.252-265

Abstract

This research aims to compare two methods of forecasting, i.e Multiple Linear Regression (MLR) and Genetic Algorithm-Neural Network (GA-NN), in forecasting dividend payout of Indonesian manufacturing company listed on Indonesia Stock Exchange from 2010-2014. Having collected 1384 firm-year observations, the result shows that these two methods could be used to predict dividend payout by considering earnings, free cash flow, growth opportunity, leverage, liquidity and size. This resesarch finds that even though both methods are powerful in prediction, yet in this case, MLR outperforms GA-NN. Keywords : Forecasting, Genetic Algorithm-Neural Network (GA-NN), Multiple Linear Regression (MLR), Dividend Payout Policy , Indonesian Manufacturing Companies
SISTEM INFORMASI PENDAFTARAN PESERTA DIDIK BERBASIS WEB (SIPD-WEB) PADA SLA TOMPASO Stenly Richard Pungus, S.Kom, M.T, M.M; Favian Joshua Mulumbot; Lidya Chitra Laoh; Debby Erce Sondakh; Reymon Rotikan
CogITo Smart Journal Vol. 9 No. 1 (2023): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v9i1.477.193-206

Abstract

Sekolah Lanjutan Advent (SLA) Tompaso merupakan sebuah sekolah swasta yang menyediakan tiga satuan pendidikan, yakni SMP, SMA, dan SMK Advent Tompaso. Sekolah ini berstatus berasrama dan menampung siswa dari berbagai daerah di Indonesia. Namun, saat ini pendaftaran peserta didik di SLA Tompaso masih dilakukan dengan cara manual, yakni peserta didik dan orang tua harus datang ke sekolah untuk melakukan pendaftaran setiap semester. Hal ini menyebabkan kesulitan bagi peserta didik yang berasal dari daerah jauh karena memerlukan waktu dan biaya yang relatif besar. Selain itu, sering terjadi kejadian formulir pendaftaran yang tercecer atau hilang, sehingga mempersulit proses pendaftaran. Berdasarkan hasil wawancara, SLA Tompaso membutuhkan sistem informasi pendaftaran peserta didik yang dapat memudahkan proses pendaftaran, hemat waktu, praktis, dan hemat biaya. Selain itu, pihak sekolah juga membutuhkan informasi pendaftaran peserta didik secara cepat. Oleh karena itu, dilakukan perancangan sistem informasi pendaftaran peserta didik berbasis web dengan menggunakan metode waterfall. Dalam pengembangan sistem informasi pendaftaran, penggunaan metode waterfall dapat memastikan tahap-tahap pengembangan dilakukan secara sistematis dan terstruktur. Sistem informasi pendaftaran yang dibuat akan memudahkan proses pendaftaran peserta didik, serta memungkinkan data peserta didik untuk disimpan dalam database. Dengan adanya sistem ini, informasi mengenai peserta didik dapat diperoleh dengan mudah dan cepat. Sehingga diharapkan, sistem informasi pendaftaran peserta didik ini dapat membantu SLA Tompaso dalam meningkatkan efisiensi proses pendaftaran dan pengelolaan data peserta didik.
Deep Learning for Peak Load Duration Curve Forecasting George Morris William Tangka; Lidya Chitra Laoh
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.694.603-612

Abstract

As the energy landscape changes towards renewable energy sources and smart grid technologies, accurate prediction of peak load duration curve (PLDC) becomes crucial to ensure power system stability. The background to this research is the urgent need for more effective prediction methods to manage increasingly complex energy loads. This research presents a leading-edge approach to PLDC prediction, leveraging Deep Learning, a subsection of artificial intelligence. Focusing on data from the Taiwan State Electric Company, this study uses a Long Short-Term Memory (LSTM) network to capture complex load patterns. The LSTM model, consisting of two layers and trained on 2019-2020 data, demonstrated excellent accuracy with a Mean Absolute Percentage Error (MAPE) as low as 0.03%. These results confirm the potential of Deep Learning to revolutionize PLDC predictions in complex energy systems. These research recommendations involve exploring diverse datasets, integrating real-time data streams, and conducting comparative analyses for more reliable prediction methodologies. The benefits of this research include providing relevant insights for sustainable energy resource management amidst a dynamic energy landscape.
Co-Authors Diaprilia Sondakh Favian Joshua Mulumbot George Morris William Tangka Green Arther Sandag Makaweks, Yanes Theodora Manaha, Cheisya Framita Lady Reymon Rotikan Sondakh, Debby Erce Stenly Richard Pungus Valentine Lumingkewas