Article Per Year (5 Year)
p-Index From 2021 - 2026
2.319
P-Index
This Author published in this journals
All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Onoma: Pendidikan, Bahasa, dan Sastra Jurnal Varian STATISTIKA
Puspita Kartikasari
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 20 Documents
Search

 1   2 
Pemanfaatan Aplikasi Berbasis Website dalam Aktifitas Kajian Harian bagi Remaja Masjid Tri Sagirani; Puspita Kartikasari; Rahayu Arya Shintawati
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 5 No 1 (2020): Juni
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-dinamika.v5i1.1404

Abstract

Remaja masjid adalah perkumpulan pemuda masjid yang melakukan aktivitas sosial dan ibadah di lingkungan suatu masjid. Remaja masjid juga memiliki tujuan dalam mendukung pembentukan kepribadian dan karakter seorang remaja, salah satunya melalui pembentukan budi pekerti, akhlak mulia, kejujuran, rasa bertanggung jawab dan menghormati orang lain. Remaja masjid mutlak keberadaannya dalam menjamin estafet makmurnya suatu masjid sehingga fungsi dinamika masjid itu sendiri dapat di pertahankan kelangengannya. Pengurus remaja masjid merasa kajian yang dilakukan selama ini masih sangat kurang dari sisi jumlah pertemuan. Pengurus berusaha untuk tetap mengikat ilmu yang dimiliki oleh anggota dalam kesehariannya, maka pengurus Remaja Masjid menginginkan pertemuan rutin yang dapat dilakukan dalam tiap harinya. Dengan bantuan teknologi informasi dan komunikasi harapannya keinginan ini dapat terwujud. Tujuan dari pelaksanakaan kegiatan ini adalah membangun suatu aplikasi berbasis website yang mampu melakukan distribusi materi kajian harian dan mampu melakukan pengukuran terhadap pemahaman materi yang telah dicapai oleh anggota remaja masjid. Target yang diharapkan dari kegiatan ini adalah peningkatan proses pembelajaran ilmu Agama bagi anggota remaja masjid dengan bantuan teknologi informasi dan komunikasi. Terdapat enam tahapan untuk mencapai tujuan yaitu (1) observasi, pengamatan terhadap kebutuhan anggota remaja masjid, (2) penyusunan rencana pembelajaran, (3) penyusunan modul pembelajaran online, (4) penyusunan aplikasi berbasis website, (5) memberikan sosialisasi dan pelatihan kepada pengurus dan anggota remaja masjid, (6) Evaluasi pelaksanaan. Hasil identifikasi pada 135 remaja diperoleh hasil 59,3 % remaja menggunakan aplikasi berbasis website untuk mengenal lebih dalam Ilmu Agama, dan 73,3% remaja menyatakan membutuhkan aplikasi berbasis website yang dapat mereka gunakan untuk kajian harian dalam mempelajari ilmu Agama secara khusus. Keberhasilan program learning community dengan memanfaatkan aplikasi berbasis website ini dapat diduplikasi untuk komunitas serupa di seluruh wilayah Indonesia.
PEMODELAN TOPIK PADA KELUHAN PELANGGAN MENGGUNAKAN ALGORITMA LATENT DIRICHLET ALLOCATION DALAM MEDIA SOSIAL TWITTER Diandra Zakeshia Tiara Kannitha; Mustafid Mustafid; Puspita Kartikasari
Jurnal Gaussian Vol 11, No 2 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v11i2.35474

Abstract

Large scale social restrictions (PSBB) is a policy issued by the Government of Indonesia as one of the efforts to reduce the spread of the Covid-19 virus. The impact of the policy is that it requires people to conduct activities online . This makes the internet users in Indonesia in the year 2020 up to 73.7%. Each provider must be able to determine strategies in order to maintain the quality of service and customer loyalty. Good reputation for the company is also important, so customers want to use internet services through their company. One of them is by listening to the complaints of the customers towards the company. In this research, modeling the topic of customer complaints carried out using the Latent Dirichlet Allocation Algorithm. The Latent Dirichlet Allocation Algorithm was chosen because the method has good performance. The topic modelling process is carried out using the gibbs sampling estimation. The topic that is often complained to First Media is that internet was turns off while working, while for IndiHome is that the internet often turns off and disconnect. Based on the results of the interpretation, 70% for First Media and 81,81% for IndiHome that these topics had been in accordance with what is complained by customers through their tweets. From the topic that have been known, it can be used as an evaluation for their company in order to maintain service quality and customer loyalty
KLASIFIKASI MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN RANDOM FOREST UNTUK DETEKSI AWAL RISIKO DIABETES MELITUS Chea Zahrah Vaganza Junus; Tarno Tarno; Puspita Kartikasari
Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.3.386-396

Abstract

Diabetes Mellitus is one of the four leading causes of death and therefore possible treatments are of crucial importance to the world leaders. Prevention and control of Diabetes Mellitus are often done by implementing a healthy lifestyle. Thus, both people with risk factors and people diagnosed with Diabetes Mellitus can control their disease in order to prevent complications or premature death.. For a proper education and standardized disease management the early detection of Diabetes Mellitus is necessary, which led to this conducted study about the classification of early detection of Diabetes Mellitus risk by utilizing the use of Machine Learning. The classification algorithms used are the Support Vector Machine and Random Forest where the performance analysis of the two methods will be seen in classifying Diabetes Mellitus data. The type of data used in this study is secondary data obtained from the official website of the UCI Machine Learning Repository consisting of 520 diabetes patient data taken from Sylhet Diabetic Hospital in Bangladesh with 16 independent variables and 1 dependent variable. The dependent variable categorizes the test result into positive and negative Diabetes Mellitus classes. The results of this study indicate that the Random Forest classification algorithm produces a better classification performance on Accuracy (98.08%), Recall (97.87%), Precision (98.92), and F1_Score (88.40%).
PENERAPAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) UNTUK MERAMALKAN PENERBANGAN DOMESTIK PADA TIGA BANDAR UDARA DI PULAU JAWA Adinda Putri Muzdhalifah; Tarno Tarno; Puspita Kartikasari
Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.3.332-343

Abstract

The number of flights is a thing to measure the marketing performance of aviation services. Forecasting the number of flights is done so that airlines can make decisions in increasing the number of passengers and revenue. Forecasting the number of flights at various airports has relationship between time and location. The suitable method for forecasting the number of flights is Generalized Space Time Autoregressive (GSTAR) method. GSTAR is a method that used for forecasting time series data that has a relationship between time and location and has heterogeneous characteristics. This study applied the GSTAR method to model and forecast the number of domestic flights at three airports in Java, namely Husein Sastranegara Airport Bandung, Ahmad Yani Semarang, and Juanda Surabaya. The research chose those three airports because the impact of Covid-19 is very severe in that area. The weight used in this study is the distance inverse weight. The resulting model is a model with differencing 1, autoregressive order 1, and spatial order limited to 1 so that the model formed is the GSTAR model (11)-I(1). The GSTAR (11)-I(1) meets the assumptions of residual white noise and normal multivariate. The model also has sMAPE values for each airport: 2.60%, 4.18%, and 9.89%. Therefore, it can be concluded that the forecasting results of Husein Sastranegara Airport Bandung, Ahmad Yani Airport Semarang, and Surabaya Juanda Airport are very accurate.
PENGARUH KONVEKSITAS TERHADAP SENSITIVITAS HARGA JUAL DAN DELTA-NORMAL VALUE AT RISK (VAR) PORTOFOLIO OBLIGASI PEMERINTAH MENGGUNAKAN DURASI EKSPONENSIAL Putri Devitasari; Di Asih I Maruddani; Puspita Kartikasari
Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.4.532-541

Abstract

Bonds are one of the investment instruments issued by the issuer as proof of debt.  Bond investment is relatively safe, but it is possible for investors to experience losses. Investors should always consider that trading a bond is always risky. One of the important bond risks is interest risk. The concept of duration can only explain well for small changes in interest rates but cannot explain well for large changes in interest rates. The estimation of the duration concept will have a larger calculation error with the greater changes in market interest rates that occur so it is necessary to add convexity to improve accuracy. This study aims to estimate the risk of government bonds based on the estimation of bond prices with the effect of convexity. Several studies have shown that exponential duration can predict bond prices more accurately than Macau duration. Exponential duration with convexity will be applied in this study to measure the accurate value of bond prices caused by changes in interest rates. The Delta-Normal VaR portfolio method is used to calculate risk based on estimated bond prices in the form of a portfolio. The formation of this portfolio aims to reduce the losses suffered by investors. This method is applied to four Indonesian government bonds with codes FR0056, FR0059, FR0074, and FR0080. The results showed that the bonds portfolio FR0056 and FR0074 had the smallest risk compared to other portfolios with a weight proportion of 15% for bonds FR0056 and 85% for bonds FR0074.
PEMODELAN TOPIK ULASAN APLIKASI NETFLIX PADA GOOGLE PLAY STORE MENGGUNAKAN LATENT DIRICHLET ALLOCATION Gina Rosalinda; Rukun Santoso; Puspita Kartikasari
Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.4.554-561

Abstract

The vast amount of review data available on the Google Play Store can be utilized to extract hidden essential information. These reviews have an unstructured format that requiring particular methods to automatically collect and analyze the review data. Topic modeling is an extension of text analysis that can find main themes or trends hidden in large sets of unstructured documents. This study applies topic modeling with the Latent Dirichlet Allocation (LDA) method to Netflix application review data sourced from the Google Play Store web. The Latent Dirichlet Allocation (LDA) method is a generative probabilistic model from textual data that can explain the hidden semantic themes in the review document. This research aims to analyze hidden topics that application users discuss. These hidden topics contain essential valuable information for Netflix users and the company. Users can use this information to decide before using Netflix services. Meanwhile, Netflix can use this information to improve the quality of its services. This research use data from a web scraping Netflix review on the Google Play Store from January 2021–August 2021. The results of topic modeling show that of the twelve topics generated, the most discussed topic by users is payment methods.
PENDEKATAN MODEL KMV MERTON UNTUK PENGUKURAN NILAI RISIKO KREDIT OBLIGASI EXPECTED DEFAULT FREQUENCY (EDF) DILENGKAPI GUI R Agil Setyo Anggoro; Mustafid Mustafid; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.1.92-103

Abstract

Bonds are debt securities from the issuer to bondholders with a promise to pay off the principal and the coupon at maturity. Bond investing can generate income while also posing investment risks. One of the risks connected with bond investing is credit risk, which might manifest as a firm collapsing (default). The KMV Merton model approach is one method of measuring bond credit risk. This Merton KMV model computes the Expected Default Frequency (EDF), which is the likelihood of a firm failing in the following years or years. The data processing system using the Graphical User Interface (GUI) can facilitate the analysis process by implementing the Shiny Package in the R studio program. This research case makes use of up to 48 months of monthly corporate asset data from January 2018 to December 2021. The results obtained the value of Expected Default Frequency (EDF) in each company, namely PT Bank Mandiri Tbk obtained a value of 0% and PT Bank Rakyat Indonesia Tbk obtained a value of 1,406668E-113%. Because PT Bank Rakyat Indonesia Tbk's percentage return is higher than that of PT Bank Mandiri Tbk, investors would be better off investing in bonds at PT Bank Mandiri Tbk.
PERAMALAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA MENGGUNAKAN METODE HOLT WINTERS EXPONENTIAL SMOOTHING DAN FUZZY TIME SERIES MARKOV CHAIN Santa Agata Mendila; Iut Tri Utami; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.1.104-115

Abstract

One of the public transportation choices by the public is the train. The number of train passengers on the island of Java often increases and decreases in certain months. PT.KAI can monitor the number of train passengers by forecasting. Forecasting aims to predict the number of train passengers so that PT.KAI is ready to provide the best service. This study uses monthly data on the number of train passengers on Java Island from January 2015 to February 2020. This study uses multiplicative holt winters exponential smoothing and fuzzy time series markov chain. The multiplicative Holt Winters exponential smoothing method is used on data that contains trend and seasonal elements that experience data fluctuations simultaneously. The fuzzy time series markov chain method is a combination of the fuzzy time series with the markov chain which aims to obtain the greatest probability using the transition probability matrix. Based on the analysis results, it can be concluded that the multiplicative holt winters exponential smoothing method is better at predicting the number of train passengers on Java Island because the value of sMAPE multiplicative holt winters exponential smoothing is smaller, it is 3,0643% and the sMAPE fuzzy time series markov chain value is 5,2955%.
Classification of Public Opinion on Social Media Twitter concerning the Education in Indonesia Using the K-Nearest Neighbors (K-NN) Algorithm and K-Fold Cross Validation Intan Monica Hanmastiana; Budi Warsito; Rita Rahmawati; Hasbi Yasin; Puspita Kartikasari
Statistika Vol. 21 No. 2 (2021): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v21i2.297

Abstract

Developing country is a country that has perspective and idea which reflect its awareness of the importance of advancing the education sector. Assessment of the quality of education in Indonesia from the perspective of the community gets different responses. Therefore, it makes people respond differently. The community response is often found on social media, one of which is Twitter. Twitter is one of the application service that is popular due to its uses to interact and communicate with people in daily life. The sentiment analysis on Twitter can be a choice to see the community’s responses to the condition of education in Indonesia. The responses are classified into positive sentiments and negative sentiments using the K-Nearest Neighbors (K-NN) algorithm with a 10-fold cross validation model evaluation. K-NN has several advantages, they are fast training, simple, easy to learn, resistance toward training data which has noise, and effective if the training data is large. In this study, the sentiment classification uses Cosine Similarity distance measurement and four k value parameters which are 3, 5, 7, and 9. Data labelling is done manually and done by scoring sentiment. Visualization of positive and negative sentiments use Word Cloud. The test results show that public sentiment about education tends to be positive on Twitter and the parameter k = 7 obtained the highest accuracy value in data labelling that was done manually and done by scoring sentiment. In labelling data manually, it obtained an accuracy of 76.93% whereas, in labelling the data with scoring sentiment, it obtained an accuracy of 77.87%. Sentiment analysis is made using the RStudio programming language as the support software.
Citra Pembelajaran Berbasis Teknologi di Perguruan Tinggi; Kendala dan Mutu Pembelajaran Online Mohammad Dika Raswadi; Puspita Kartikasari; Rizwan Arisandi; Ramly Ramly
Jurnal Onoma: Pendidikan, Bahasa, dan Sastra Vol. 11 No. 1 (2025)
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/onoma.v11i1.5451

Abstract

Perkembangan teknologi dan ilmu pengetahuan memberi kemudahan dalam menyelesaikan pekerjaan termasuk bidang pendidikan. Dalam era Revolusi Industri 4.0 penggunaan teknologi pada aspek pendidikan menggeser praktik pembelajaran konvensional ke berbasis teknologi. Platform online yang digunakan membuat pembelajaran lebih mudah dan fleksibel sambil dipersepsikan memiliki kendala dan penurunan mutu proses. Penelitian ini mengkonfirmasi temuan sebelumnya yang mencitrakan permasalahan dalam pembelajaran on line berlaku relatif sama pada semua kelompok sasaran penelitian tanpa mempertimbangkan stratifikasi dan klaster pengguna. Tujuan penelitian ini adalah mendeskripsikan citra pembelajaran dengan teknologi dalam pembelajaran online, mutu komunikasi timbal balik dosen-mahasiswa, kedisiplinan, dan aktivitas individu dalam belajar. Populasi penelitian mencakup seluruh mahasiswa perguruan tinggi negeri dan swasta di ibu kota Provinsi Sulawesi Selatan dan Sulawesi Barat, Indonesia. Sampel terdiri dari 255 mahasiswa yang dipilih secara acak. Data dikumpulkan dengan angket dan dianalisis secara deskriptif. Hasil penelitian mengungkap bahwa mahasiswa mempersepsi adanya kendala namun tidak signifikan saat pembelajaran. Mutu komunikasi timbal balik dosen-mahasiswa maupun antarmahasiswa relatif sama dengan saat pembelajaran tatap muka, belajar individual mahasiswa juga demikian, kedisiplinan menurun dibandingkan saat pembelajaran tatap muka. Perlu penanaman sikap positif dan kontrol perilaku mahasiswa agar pelaksanaan pembelajaran Online menjadi lebih optimal.
Co-Authors Adinda Putri Muzdhalifah Agil Setyo Anggoro Alan Prahutama Asri Lutfia Silmi Bramesa Winanda Nugraha Budi Warsito Chea Zahrah Vaganza Junus Deanira Qinanty Alamsyah Di Asih I Maruddani Diandra Zakeshia Tiara Kannitha Dwi Ispriyanti Felinda Arumningtyas Gina Rosalinda Hasbi Yasin Indah Suryani Intan Monica Hanmastiana Iut Tri Utami Khoirul Anam Mohammad Dika Raswadi Mustafid Mustafid Nabila Surya Wardani Putri Devitasari Rahayu Arya Shintawati Ramly Ramly Rita Rahmawati Rizwan Arisandi Rosinar Siregar Rukun Santoso Santa Agata Mendila Sudarno Sudarno Tarno Tarno Tatik Widiharih Tri Sagirani Yashmine Noor Islami Yayan Setiawan