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PREDIKSI HARGA KELAPA SAWIT MENGGUNAKAN METODE REGRESI LINEAR BERGANDA: Studi Kasus PT. Bakrie Sumatera Plantations, Tbk. Eliza, Agnes Lasmaria; Manalu, Darwis Robinson; Yohanna, Margaretha
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp89-95

Indonesia's plantation sector, especially palm oil, has a high selling value and its attractiveness continues to increase. The price of palm oil is strongly influenced by external factors, especially the price of crude oil (CPO) and palm oil production globally. Unstable price fluctuations have become a challenge for companies such as PT Bakrie Sumatera Plantations, Tbk. in planning harvest time and optimizing profits. To overcome this uncertainty, this study proposes the use of the Multiple Linear Regression method to predict palm oil prices. Price and production data from 2021 to 2023 are the main basis for developing the prediction model. In this study, 4 independent variables were used, namely year, month, oil price and total production. The amount of data used in this study is 33 data. In testing the Multiple Linear Regression method has an RMSE value of 0.24 and the predicted value of palm oil prices from January to December 2024 is the price of palm oil with the highest value in January 2024 of Rp. 2,276.931 / kg and with the lowest value in December 2024 of Rp. 1,474.75 / kg.

Pengenalan Aplikasi Kahoot! Bagi Guru Dan Siswa-Siswi Pada SMA GKPI Padang Bulan Medan Larosa, FGN Larosa; Sitepu, Surianto; Saragih, Naikson F.; Situmorang, Alfonsus; Dumayanti, Imelda Sri; Naibaho, Jimmy F.; Manurung, Samuel; Jaya, Indra Kelana; Yohanna, Margaretha; Rumahorbo, Benget; Simanullang, Harlen; Sinambela, Marzuki; Silalahi, Veraci
Jurnal Pengabdian Masyarakat Nauli Vol. 2 No. 1 (2023): Agustus, Jurnal Pengabdian Masyarakat Nauli
Publisher : Marcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/nauli.v2i1.102

interaksi antar personal, yang mempengaruhi interaksi antara guru/pendidik dengan siswa-siswi, bersifat praktis, menarik dan mudah dibawa-bawa, sangat populer, dan banyak dipakai pada pembelajaran dengan metode Blended Learning. Salah satu aplikasi yang dimaksud adalah Kahoot!, yang sangat mudah dalam pemasangan bahkan gratis. Kahoot! mulai menyebar ke berbagai pembelajaran seperti Bahasa Indonesia, Bahasa Inggris, dan Kimia. Hasil penelitian menunjukkan bahwa Kahoot! mampu tampil sebagai media permainan digital berbasis pembelajaran, di mana Kahoot! memberikan persepsi positif dalam efektivitas pembelajaran, ketertarikan dalam aktvitas pembelajaran dan motivasi dalam aktivitas pembelajaran

Penerapan Algoritma K-Nearest Neighbors dalam Mengklasifikasi Penyakit Multiple Sclerosis Yohanna, Margaretha; Sitompul, Andrew Efraim Nicholas; Silalahi, Arina Prima
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp307-315

The central nervous system is impacted by multiple sclerosis (MS), a chronic autoimmune disease that requires early identification for successful treatment. Because of its many symptoms and similarities to other neurological disorders, MS can be difficult to diagnose. Artificial intelligence techniques like the K-Nearest Neighbors (KNN) algorithm can be used to help with quicker and more precise classification in order to solve this problem. The goal of this study is to classify MS using the KNN technique and assess how well it performs in this regard. The Kaggle platform provided the dataset, which consists of 273 patient records with 18 clinical characteristics. With k = 3 as the number of neighbors, the data was split into 80% for training and 20% for testing. The Python programming language was used to implement the classification procedure. According to the findings, the KNN algorithm classified MS with an accuracy of 81.82%. The precision, recall, and f1-score for class 1 were 0.83, 0.76, and 0.79, respectively, according to additional analysis utilizing a classification report, whereas the scores for class 2 were 0.81, 0.87, and 0.84. These findings suggest that the KNN method has the potential to serve as a supportive tool in the diagnosis of Multiple Sclerosis.

PENERAPAN METODE RANDOM FOREST DALAM MENDETEKSI BERITA HOAX Tambunan, Tio; Yohanna, Margaretha; Silalahi, Arina Prima
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp301-306

Hoax is information that is not true. The Ministry of Communication and Informatics (Kominfo) found that there was 2,099 hoax news that was spread thousands of times via social media. This generally impacts the community so it can lead to a crisis of confidence in the government. This arises because many message recipients have different literacy levels, which will affect how people analyze the information conveyed. This research uses the Random Forest method, which is used to classify large amounts of data to detect hoax news. The research results show that the Random Forest method is proven to be able to classify hoax news based on data that has been weighted and entered into the system. From the results of the study using 200 data sets, which were divided by 80% in the form of training data and 20% of testing data, the classification results obtained from the testing data were in the form of 28 positive sentiments and 23 negative sentiments with an accuracy rate of 98%.

ANALISIS SENTIMEN TERHADAP APLIKASI LINKAJA MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Sitepu, Marheni; Yohanna, Margaretha; Manurung, Samuel Van Basten H.
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp44-50

Electronic money is increasing, causing more and more service innovations to emerge. People carry out online transactions using electronic technology, or Fintech. The fintechs widely used today are e-wallets or digital wallets such as Dana, Ovo, LinkAja, etc. In this research, LinkAja as a fintech application will be analyzed using 100 review data samples and summarized into two classes: positive and negative. This research was carried out using the Naïve Bayes Classifier classification method. Sentiment analysis of reviews on the LinkAja application obtained results of 75% accuracy, 83% Precision, 75% Recall, and 73% F1_score.

PREDIKSI HARGA KELAPA SAWIT MENGGUNAKAN METODE REGRESI LINEAR BERGANDA: Studi Kasus PT. Bakrie Sumatera Plantations, Tbk. Eliza, Agnes Lasmaria; Manalu, Darwis Robinson; Yohanna, Margaretha
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp89-95

Indonesia's plantation sector, especially palm oil, has a high selling value and its attractiveness continues to increase. The price of palm oil is strongly influenced by external factors, especially the price of crude oil (CPO) and palm oil production globally. Unstable price fluctuations have become a challenge for companies such as PT Bakrie Sumatera Plantations, Tbk. in planning harvest time and optimizing profits. To overcome this uncertainty, this study proposes the use of the Multiple Linear Regression method to predict palm oil prices. Price and production data from 2021 to 2023 are the main basis for developing the prediction model. In this study, 4 independent variables were used, namely year, month, oil price and total production. The amount of data used in this study is 33 data. In testing the Multiple Linear Regression method has an RMSE value of 0.24 and the predicted value of palm oil prices from January to December 2024 is the price of palm oil with the highest value in January 2024 of Rp. 2,276.931 / kg and with the lowest value in December 2024 of Rp. 1,474.75 / kg.

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