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All Journal CogITo Smart Journal Jurnal Multidisiplin Madani (MUDIMA) Klabat Journal of Management Servitium Smart Journal
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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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
Celebrity Endorsements and Millennial Purchase Decisions on Instagram Lidya Chitra Laoh
Jurnal Multidisiplin Madani Vol. 2 No. 12 (2022): December 2022
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/mudima.v2i12.1945

Abstract

This study aims to analyze the influence of celebrity endorsements on purchasing decisions for millennials on Instagram. To answer the research questions, data were collected on 471 students as research objects using a questionnaire. A simple regression method was used to analyze the collected data. The results of this study's data analysis found that celebrity endorsements were influential in determining millennial generation purchasing decisions
PENGARUH SOSIAL MEDIA MARKETING TERHADAP PENJUALAN USAHA MIKRO KECIL DAN MENENGAH (UMKM) Diaprilia Sondakh; Valentine Lumingkewas; Lidya Chitra Laoh; Green Arther Sandag
Klabat Journal of Management Vol 3 No 1 (2022): Klabat Journal of Management
Publisher : Faculty of Economics and Business, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (636.874 KB) | DOI: 10.60090/kjm.v3i1.823.67-82

Abstract

Social media does not only use for communication and sharing, but has now become a marketing media for business owners. The use of social media as a marketing media by business owners called UMKM occurs in Langowan District, Minahasa Regency, North Sulawesi Province. The purpose of this study was to determine the sentiments of Facebook users towards marketing carried out by UMKM and the influence of Facebook's marketing on increasing UMKM sales. This study uses two analyzes, namely sentiment analysis and multiple linear regression analysis. The sample of this research is 61 food and beverage UMKM that use Facebook as a marketing media. The data sources used in the sentiment analysis are Facebook user comments on UMKM uploads, the data sources in the multiple linear regression analysis are financial reports along with the upload frequency and the number of reactions from UMKM social media marketing activities. As a result, sentiment analysis in this study classifies positive comments as higher than negative comments with the application of the LSTM model, multiple linear regression analysis finds that social media marketing has a positive and significant effect on increasing UMKM sales. Social media tidak hanya mengambil peranan sebagai media komunikasi dan berbagi, namun saat ini telah menjadi media pemasaran atau marketing di kalangan pelaku usaha. Penggunaan media sosial sebagai sarana pemasaran oleh para pelaku usaha yang disebut dengan UMKM terjadi di Kecamatan Langowan, Kabupaten Minahasa, Provinsi Sulawesi Utara. Tujuan dari penelitian ini untuk mengetahui sentimen dari pengguna Facebook terhadap marketing yang dilakukan oleh UMKM dan pengaruh social media marketing Facebook terhadap peningkatan penjualan UMKM. Penelitian ini menggunakan dua analisis yaitu analisis sentimen dan analisis regresi linier berganda. Sampel dari penelitian ini adalah 61 UMKM makanan dan minuman yang menggunakan Facebook sebagai media marketing. Sumber data yang digunakan dalam analisis sentimen adalah komentar pengguna Facebook dalam unggahan UMKM, sedangkan sumber data dalam analisis regresi linier berganda adalah laporan keuangan beserta frekuensi unggahan dan jumlah reaction dari aktivitas social media marketing UMKM. Hasilnya, analisis sentimen dalam penelitian ini mengklasifikasikan komentar positif lebih tinggi dibandingkan komentar negatif dengan penerapan model LSTM, sedangkan analisis regresi linier berganda menemukan bahwa social media marketing berpengaruh positif dan signifikan terhadap peningkatan penjualan UMKM. Keywords: Facebook, Multiple linear regression analysis, Sentiment analysis, Social media marketing, UMKM
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.
Mobile-Based Integrated Management Application for Sick Toddlers at Puskesmas Sumalata Makaweks, Yanes Theodora; Manaha, Cheisya Framita Lady; Laoh, Lidya Chitra
Jurnal Multidisiplin Madani Vol. 4 No. 7 (2024): July 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/mudima.v4i7.10692

Abstract

Infant mortality is a critical public health indicator. Among other efforts to reduce this figure is the improved health skills of treating sick toddlers, and the Sumalata center was a of the centers located in the north Gorontalo district. There was a strategy in the Sumalata center for reducing toddler mortality, pain, and disability. People may employ apps to get information for counseling, immunization registration, and outpatient registration. Researchers used prototype methods beginning with the development phase of the communication process, data collection, quick plan design, modeling quick design identifying system needs, construction of the prototype of deployment delivery and feedback development on this stage, feedback from the puskesmas Sumalata. This system was designed with javascript as a programming language, alternative native as a user interface display on the front-end, nodejs, and mysql as a database on the back-end. The applications developed allow patients to enroll in immunization, register for clinical care, and obtain information from the Sumalata Medical Center
Literasi Digital dan Etika Penggunaan Artificial Intelligence untuk Siswa Sekolah Menengah Kejuruan Argha Orion Silitonga; Debby Erce Sondakh; Raissa Maringka; Jacquline Waworundeng; Lidya Chitra Laoh
Servitium Smart Journal Vol 4 No 2 (2026): Servitium Smart Journal
Publisher : Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/servitium.v4i2.49

Abstract

This Community Service Program (PKM) conducted by the Faculty of Computer Science at Universitas Klabat aims to enhance digital literacy and understanding of Artificial Intelligence (AI) ethics among students of SMK Negeri 1 Ratahan. Using a participatory-educational approach, the program includes interactive seminars, group discussions, case study analysis, and demonstrations of AI technologies and Large Language Models (LLM). Participants gained knowledge of basic AI concepts, ethical principles such as fairness, transparency, and accountability, as well as the social implications of AI usage. Evaluation results indicate a significant improvement in students’ understanding and awareness of AI ethics, reflected through post-test outcomes and participant feedback. The program not only broadened students’ technological insights but also fostered critical thinking and responsible digital behavior. This initiative is expected to serve as a foundation for continued collaboration between the school and higher education institutions in strengthening digital literacy and AI ethics education at the vocational secondary level.
Co-Authors Argha Orion Silitonga Diaprilia Sondakh Favian Joshua Mulumbot George Morris William Tangka Green Arther Sandag Jacquline Waworundeng Makaweks, Yanes Theodora Manaha, Cheisya Framita Lady Maringka, Raissa Reymon Rotikan Sondakh, Debby Erce Stenly Richard Pungus Valentine Lumingkewas