Pemodelan Prakiraan Tingkat Inflasi Di Indonesia Dengan ARIMA

Penelitin bertujuan untuk mengetahui nilai inflasi bulanan yang terjadi di Indonesia. Penelitian menggunakan data sekunder yang sumber dari BPS dan Bank Indonesia. Sampel penelitian diambil mulai periode Januari 2010 sampai April 2021. Metode analisis data dengan model ARIMA.  Dalam proses analisis data dibagi menjadi dua bagian yaitu data training (Januari 2010 – Desember 2020) sebagai data bangkitan untuk membangun model dan data testing (Januari – April 2021) untuk menguji hasil prediksi dari model. Dari analisis data diperoleh hasil pemodelan ARIMA (3,1,2). Uji validasi model dengan parameter RMSE (Root Mean Square Error)  sebesar 1.076, nilai MAE (Mean Absolute Error) sebesar 0.696, dan MAPE (Mean Absolute Percentage Error) sebesar 220.68.  Uji validasi hasil prediksi dengan uji rata-rata dan varian menunjukkan bahwa hasil pengujian dari kedua metode tersebut mempunyai nilai probabilitas yang lebih besar dari 0,05 sehingga dapat disimpulkan tidak terdapat perbedaan yang signifikan nilai aktual dengan nilai prediksinya. Mengingat model ini mempunyai keterbatasan, maka disarankan untuk meningkatkan akurasi model prediksi dapat dilakukan dengan pendekatan metode lain, misalnya naive bayes atau metode jaringan saraf tiruan (artificial neural network)

Financial Contagion and Good Corporate Governance on Bank Companies Performance in Indonesian Stock Exchange

This study aims to examine the effect of fianancial contagion and good corporate governance on company performance of banks company listed on  Indonesia Stock Company. Corporate governance is measured using the number of independent commissioners, frequency of board meetings, and attendance at board meetings. This study has two dependent variables, namely market performance as measured by Price Earning Ratio (PER) and operational performance as measured by return on equity (ROE). The analysis method used is multiple regression models with two dependent variables. The results showed that the contagion effect had a positive influence on the company's PER performance but did not have an effect on the company's ROE performance. Meanwhile, corporate governance through the board of directors' meeting is able to have an influence on ROE performance but not on PER. This shows that when there is a domino effect from another country it will have an influence on share prices in the market

Sistem Pendukung Keputusan Untuk Menentukan Siswa Calon Peserta Olimpiade Dengan Metode VIKOR dan MOORA

Students participation at Paramarta Junior High School for the past several years in the olympic competition has received unsatisfactory result. Achievement achieved seems to be lacking because it does not have a good parameter in the selection of an honor student. The decision to vote is based solely on the results of math teacher’s deliberations. This research was intended to select the best students who will be sent to the Olympic competition in mathematics using the VIKOR (Vise Kriterijumska Optimizacija I Kompromisno) and MOORA (Multi-Objective Optimization on the basic of Ratio Analysis) methods. The research was conducted in Paramarta Junior High School with objects on students as a sample. The study sample selected based on the academic value of grade 7 (seven) of 156 students then selected by selected by the math teacher’s deliberation according to the criteria that have been established by the school before so that 8 students were obtained. Research data in the form of secondary data collected with documentation studies. The data analysis method to select one candidate participant is using VIKOR and MOORA methods. The results that obtained from both methods have the same results, 3 alternavites was selected that is a student named Arya Daffa Khalifahris with 0 VIKOR index value and 0.40 MOORA optimization value

KLASTERISASI PERSEBARAN COVID-19 DI KOTA TANGERANG SELATAN DENGAN METODE CLUSTERING K-MEANS DAN DENSITY BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE

Pandemi Covid-19 yng menyebar di Indonesia pada awal tahun 2020 telah memberikan dampak yang signifikan bagi Indonesia terutama pada sektor kesehatan. Dampak dari pendemi tersebut telah direspon oleh pemerintah dengan mengantisipasi melalui upaya kebijakan pembatasan mobilitas dan kebijakan pada semua sektor dan lini. Kota Tangerang Selatan sebagai daerah penyangga di DKI Jakarta  tentunya perlu lebih waspada dalam menangani gejala baru covid-19. tujuan penulisan paper ini berusaha melakukan analisis pada pola penyebaran virus covid-19 di Kota Tangerang Selatan. Penelitian dilakukan dengan metode kualitatif  dengan menggunakan data sekunder yang diperoleh dari web resmi https://lawancovid19.tangerangselatankota.go.id/. Sampel data Covid-19 yang digunakan adalah data dari Januari 2021 hingga Maret 2022. Hasil pengumpulan data dianalisis dengan metode K-Means dan Density Based Spatial Clustering of Applications With Noise (DBSCAN). Untuk menguji tingkat akurasi model klasterisasi diukur dengan metode Davies Bouldin Index. Hasil penelitian menunjukkan bahwa klasterisasi yang terbentuk ada 6 kluster. Kluster yang terbentuk dengan metode K-Mean kluster dengan jumlah anggota terbanyak pada kluster 0 sebanyak 12 atau sebesar 22.22%. Dengan metode DBSCAN kluster terbanyak pada kluster 4 dengan persentase sebesar 29,63% dengan jumlah anggota sebanyak 16 orang. Uji validasi data hasil pemodelan diukur dengan metode Davies Bouldin Index (DBI) dapat disimpulkan bahwa metode K-Means lebih cocok digunakan untuk mengkonstruksikan clustering Covid-19 di Kota Tangerang Selatan karena mempunyai nilai DBI yang lebih kecil dibanding dengan metode DBSCAN

Prediksi Inflow Daerah Aliran Sungai Larona Dengan Model Seasonal Autoregressive Integrated Moving Average

Larona Watershed (DAS) Inflow Prediction entering the reservoir has a very important role in managing the reservoir's water resources. Various approaches using mathematical models have been carried out, the results of which can be used as management tools to understand estimates and predictions of future inflow values, especially in the context of managing and planning water utilization for company needs at PT Vale Indonesia Tbk. The research aims to find a prediction model for the water inflow of the Towuti, Matano and Mahalona reservoirs. The research method uses a statistical approach using the SARIMA (Seasonal Autoregressive Integrated Moving Average) model. Research data, time series data, monthly inflow of the Larona watershed for January 2006 – December 2019. The research results showed that the best model was SARIMA (2,0,1)(0,1,1)12. The mathematical model prediction formulated is 4.786 + 1.459t-1 – 0.648t-2 – 0.714 e_(t-1). The model accuracy level was tested using the RMSE (Root Mean Squared Error) criteria of 0.767, MAE (Mean Absolute Error) level of 0.592, MAPE (Mean Absolute Percentage Error) of 14.58. To validate the predicted values, the F test, Siegel-Turkey, Bartlett, Levene was carried out at the α=5% level. The test results for the difference between actual and predicted values were concluded to accept the null hypothesis, which means that there is no significant difference between the actual data values and the predicted data values

Forecasting The Stock Market Movements Of Unilever Companies Jakarta During The Covid-19 Pandemic Using Artificial Neural Network

The coronavirus (Covid-19) pandemic that has hit Indonesia since March 2020 and has been spreading for a year has led to twists and turns to stock price movements in the capital market. This study aims to determine the accuracy of predictions and movements in the stock price of UNVR companies. JKT in the face of the Covid 19 pandemic. Daily stock data samples from January 01, 2019 to May 15, 2021 taken from https://finance.yahoo. com/quote/ UNVR.JK p=UNVR.JK sources. The data information in this study includes closure as a class or label. Medium attributes the opening, high, low, and volume of the company's stock as an atribut or predictor. As many as 90% datasets as data training to build models and as much as 10% datasets as data testing. Data analysis is done with Artificial Neural Network algorithm to predict the value of the company's share price. The right stock price prediction will provide knowledge information about the current status and future stock price movements. The results showed that the ANN model obtained from the experiment results was with 95% trainining data and 5% testing data. The ANN model has 5 input notes with one bias note, one hidden layer with 5 notes including one bias and produces one output that is stock closing. The validation result of RMSE value model is 62,741 and SE value is 3936.43. The accuracy of the model with a correlation coefficient value of 0.998 means there is a very strong positive correlation between the actual data and the prediction data. Keywords: Prediction, Stock Price, Artificial Neural Networ

Pengaruh Profitabilitas Terhadap Harga Saham Perusahaan Manufaktur pada Sub Sektor Industri Semen yang Terdaftar di Bursa Efek Indonesia

Saham-saham properti dan real estate terlihat lesu sejak awal tahun 2020. Penurunan nilai saham sektor properti dan real estate diperberat oleh pandemi Covid-19 yang menekan daya beli masyarakat. Penurunan penjualan properti dan real estate tersebut juga berdampak pada penurunan harga saham sub sektor industri semen. Nilai harga saham yang cukup fluktuatif pada pasar modal menjadikan suatu fenomena yang menarik untuk dikaji lebih mendalam. Penelitian ini bertujuan untuk menganalisis pengaruh Return On Asset (ROA) dan Return On Equity (ROE) terhadap harga saham secara persial maupun secara simultan pada perusahaan manufaktur sub sektor industri semen. Penelitian menggunakan data sekunder dari data saham yang tercatat di BEI. Sampel penelitian dibatasi pada periode laporan keuangan tahun  2016-2020. Variabel yang diteliti antara lain ROA, ROE dan harga saham. Analisis data dilakukan dengan metode kuantitaitf dengan model analisis regresi berganda data panel. Hasil pemilihan model terbaik melalui uji Chow dan Hausman menghasil pilihan model terbaik adalah fixed effect model. Model regresi berganda yang ditemukan adalah adalah Harga saham = 5.155,429 + 5.030,338 ROA – 543,88 ROE. Pengujian hipotesis menunjukan bahwa (1) terdapat pengaruh positif yang signifikan ROA terhadap Harga Saham (2) terdapat pengaruh negatif yang tidak signifikan ROE terhadap Harga Saham, (3) terdapat pengaruh yang signifikan ROA dan ROE secara bersama-sama terhadap harga saham. Nilai Koefisien determinan model regresi sebesar 96.8%, artinya variansi naik turunnnya harga saham dipengaruhi oleh ROA dan ROE sebesar 96,8%, serta dipengaruhi oleh faktor lain sebesar 3,2%. Kata kunci: Harga Saham, Return On Asset, Return On Equity

PENGEMBANGAN SISTEM INFORMASI DIRECT DEBIT DONOR PROGRAMME (DDDP) DENGAN PENDEKATAN INCREMENTAL LIFE CYCLE MODEL (STUDI KASUS LEMBAGA KONSERVASI LINGKUNGAN DI JAKARTA)

Direct Debit Donor Programme (DDDP) is a donation program for individuals who are interested in taking part in social activities by direct debit of donations through bank accounts or credit cards which are commonly referred to as supporters or donors. Management of a structured and integrated information system with a good database concept will support a more effective and efficient performance. This study aims to analyze and develop the DDDP information system as a solution for supporter donation data management in increasing time efficiency. Information system development is carried out in stages starting from the need for basic functions in accordance with the development stages with the Incremental model, namely Requirements, Specifications, Architecture Design, Code and Test. modeling system development using the Unified Modeling Language (UML). Data collection was carried out through observation, direct interviews with related parties, viewing documents that were related to the research being carried out and conducting literature reviews. Functional testing on the system uses the black box testing method with the test results not found any errors in the system. The result of this research is a supporter donation data management information system which is named DDDP Application System

Pengembangan Sistem Kontrol Pemilah Kematangan Buah Pisang Pada Konveyor Menggunakan Metode Klasifikasi K-Nearest Neighbors Berbasis OpenCV

This research focuses on developing a micro-controller-based banana ripeness sorting tool with the implementation of the K-Nearest Neighbors (KNN) algorithm for the classification of ripeness levels based on RGB color image processing using the OpenCV library. Banana is an important fruit in society because of their high nutritional content, but manual sorting of banana fruit is a challenge for farmers and officers. The tool built uses Arduino UNO as a controller, a conveyor belt with a dynamo motor, and a servo motor for sorting. The KNN method is used for classification based on banana skin color. The results showed that the success rate of sorting reached 100% at the neighboring value of K = 3, 93.33% at K = 5, and 86.66% at K = 1. This tool can be an efficient solution for automatically sorting bananas based on ripeness level with high accuracy

ANALISIS PERBANDINGAN METODE TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION, SIMPLE ADDITIVE WEIGHTING DAN WEIGHTED PRODUCT DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU TERBAIK

Tenaga Pendidik adalah sumber yang sangat penting bagi setiap sekolah dalam melangsungkan pendidikan anak bangsa, guru juga mempunyai tantangan sendiri bagi pihak pengelola lembaga pendidikan untuk dapat memberikan suatu keputusan yang terbaik, serta berkualiatas, guna membantu meningkatkan kualitas pendidikan dimasa yang akan datang. Namun SMPIT Rahmatutthoyyibah Al-Iflahah Kab. Tangerang, Pemilihan guru terbaiknya masih bersifat subjektif, sehingga dibutuhkan sistem pendukung keputusan untuk menentukan pemilihan guru yang terbaik yang ada di SMPIT Rahmatutthoyyibah Al-Iflahah Kab. Tangerang, dan untuk menentukan guru terbaik penulis menggunakan Metode Metode Technique for Order by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), dan Weighted Product (WP). Pemilihan Guru terbaik dinilai dari 18 responden yakni Kepala Sekolah, 6 Staf Tenaga Pendidik , 10 murid kelas 9 dan 1 perwakilan dari wali murid. Kriteria dalam pemilihan guru terbaik adalah menguasai belajar mengajar, penilaian dan evaluasi, mengenal karakteristik peserta didik, pengembangan kurikulum, etos kerja dan tanggung jawab, kedisiplinan, hubungan guru dengan teman sejawat, bersikap inklusif, objektif, serta tidak diskriminatif, hubungan guru dengan wali murid / komite sekolah, dan yang terakhir yaitu kerja sama tim. Hasil dari implementasi dari ketiga metode ini pada semua kriteria dan sub kriteria dari 5 orang guru yang dinilai, sehingga Ibu Yulia, S.Pd. yang mendapatkan peringkat terbaik dengan nilai 0,707 (Metode TOPSIS), 0,705 (Metode SAW), dan 0, 231 (Metode WP). Dan untuk hasil proses perbandingan antara metode TOPSIS, SAW, dan WP bahwa WP adalah metode yang paling sesuai dengan prosentase 99,998% daripada metode TOPSIS dan SAW.   Kata kunci: SPK, Guru Terbaik, TOPSIS, SAW, W