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InPrime: Indonesian Journal Of Pure And Applied Mathematics
Published by Universitas Islam Negeri Syarif Hidayatullah Jakarta
ISSN : 26865335     EISSN : 27162478     DOI : 10.15408/inprime
Core Subject : Science, Education,
Computer Science & IT Mathematics
InPrime: Indonesian Journal of Pure and Applied Mathematics is a peer-reviewed journal and published on-line two times a year in the areas of mathematics, computer science/informatics, and statistics. The journal stresses mathematics articles devoted to unsolved problems and open questions arising in chemistry, physics, biology, engineering, behavioral science, and all applied sciences. All articles will be reviewed by experts before accepted for publication. Each author is solely responsible for the content of published articles. This scope of the Journal covers, but not limited to the following fields: Applied probability and statistics, Stochastic process, Actuarial, Differential equations with applications, Numerical analysis and computation, Financial mathematics, Mathematical physics, Graph theory, Coding theory, Information theory, Operation research, Machine learning and artificial intelligence.
Arjuna Subject : Matematika - Matematika Terapan
Articles 7 Documents
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Search results for , issue "Vol 1, No 1 (2019)" : 7 Documents clear
Sequential Topic Modelling: A Case Study on Indonesian LGBT Conversation on Twitter Arsy Arslina; Muhaza Liebenlito
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1933.171 KB) | DOI: 10.15408/inprime.v1i1.12726

Abstract

AbstractAs a country with the largest Muslim population in the world, the Lesbian, Gay, Bisexual, and Transgender (LGBT) issue in Indonesia has always been a hot topic to investigate. Social media such as Twitter is normally the main media where people normally discuss this LGBT topic. In this paper, we collect 18,552 tweets dated from 2015 up to 2018 to analyze the dynamics of the LGBT conversation among Indonesian peoples. In this research, we will explore the main topic of the LGBT conversation using Linear Discriminant Analysis (LDA). LDA is one of the most popular methods of soft clustering. This technique is effective to identify latent topic information (hidden) in a collection of big data using a bag of words approaches that treat every document as a vector of total words and is represented as a probability distribution on several topics. The result shows that there are seven main categories that people normally talked about regarding LGBT i.e. politics, religion, government, ethics, nationality, culture, and technology. Looking at the topic probability distributions on each semester we found that it is generally homogenous. An exception occurs during the government election period where politic tends to have a significantly higher probability. In other words, we have found that there is a tendency that LGBT issues are used in Indonesian politics.Keywords: LGBT; politics; topic modeling; twitter. AbstrakSebagai negara dengan penduduk muslim terbesar di dunia, isu mengenai Lesbian, Gay, Bisexual, dan Transgender (LGBT) di Indonesia adalah isu sensitif yang senantiasa menarik untuk diteliti. Media sosial seperti twitter adalah salah satu media yang biasa digunakan masyarakat untuk mendiskusikan tentang topik LGBT ini. Penelitian ini menggunakan 18.552 tweet tahun 2015 – 2018 dikumpulkan untuk melihat perbedaan pola perbincangan dari waktu ke waktu. Dalam penelitian ini, eksplorasi topik utama perbincangan LGBT dianalisis menggunakan metode Linear Discriminant Analysis (LDA). LDA adalah metode yang paling populer dalam soft clustering. Teknik ini efektif untuk mengidentifikasi informasi topik laten (tersembunyi) dalam koleksi dokumen besar menggunakan pendekatan bag of words yang memperlakukan setiap dokumen sebagai vektor jumlah kata dan direpresentasikan sebagai distribusi probabilitas atas beberapa topik, sementara setiap topik direpresentasikan sebagai distribusi probabilitas atas sejumlah kata. Hasil menunjukkan bahwa terdapat tujuh topik dominan yang sering muncul pada perbincangan tentang LGBT, yaitu politik, agama, pemerintahan, keasusilaan, kewarganegaraan, budaya dan teknologi. Pada kategori ini kemudian distribusi probabilitas topik dihitung dan dianalisa pada setiap semesternya. Hasilnya menunjukkan bahwa ada kecenderungan distribusi topik seragam, kecuali pada masa-masa pergantian pemerintahan dimana kategori politik cenderung meningkat secara signifikan. Dengan kata lain, ada kecenderungan bahwa isu LGBT dikaitkan dengan kehidupan perpolitikan di Indonesia.Kata kunci: LGBT, politik, topic modelling, twitter.
Prediction of The Number of Ship Passengers in The Port of Makassar using ARIMAX Method in The Presence of Calender Variation Laili Nahlul Farih; Irma Fauziah; Madona Yunita Wijaya
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (833.963 KB) | DOI: 10.15408/inprime.v1i1.12786

Abstract

AbstractIndonesia is an archipelago with the largest Muslim population in the world. Every year, Indonesian people have a tradition of meeting relatives in other areas or take a vacation on Eid al-Fitr. People use different modes of transport to travel such as air, water, and land transport. Port plays a role in supporting water transportation because it is a knot of inter-regional relations. The celebration of Eid al-Fitr moves forward by about 11 days every year. The purpose of this thesis is to make an estimate of the total departure of ship passengers in the main port of Makassar using the ARIMAX method with the effects of calendar variations. The ARIMAX method is a method that can be used when there are exogenous variables, where in this case the exogenous variable is in the form of variable dummy wich is Eid holidays. These forecasting results show that the ARIMAX  method has a relatively small accuracy with the MAPE value of .Keywords: water transportation; calendar variations effects; Eid Al-Fitr. AbstrakIndonesia merupakan negara kepulauan dengan mayoritas muslim  terbesar  didunia. Setiap tahun masyarakat Indonesia memiliki tradisi bertemu sanak saudara di daerah lain ataupun berlibur pada hari raya Idul Fitri. Jalur transportasi yang digunakan yaitu melalui darat, udara dan laut. Pelabuhan memiliki peran yang sangat penting dalam mendukung transportasi laut karena menjadi titik simpul hubungan antar daerah. Perayaan hari raya Idul Fitri dalam setiap tahun mengalami pergeseran 11 hari. Tujuan penulisan skripsi ini adalah untuk membuat prakiraan total keberangkatan penumpang kapal di Pelabuhan Utama Makassar menggunakan metode ARIMAX dengan efek variasi kalender. Metode ARIMAX merupakan metode yang dapat digunakan ketika data tersebut menggunakan variable eksogen, dimana dalam kasus ini variable eksogennya berupa variable dummy libur hari raya idul fitri. Hasil peramalan ini menunjukan bahwa metode ARIMAX  memiliki tingkat akurasi yang lebih baik dibandingkan ARIMA musiman  dengan nilai MAPE sebesar 14,08%.Kata Kunci: transportasi air; efek variasi kalender, Hari Raya Idul Fitri.
Non-linear Mixed Models in a Dose Response Modelling Madona Yunita Wijaya
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (728.374 KB) | DOI: 10.15408/inprime.v1i1.12731

Abstract

AbstractStudy designs in which an outcome is measured more than once from time to time result in longitudinal data. Most of the methodological works have been done in the setting of linear and generalized linear models, where some amount of linearity is retained. However, this still be considered a limiting factor and non-linear models is another option offering its flexibility. Non-linear model involves complexity of non-linear dependence on parameters than that in the generalized linear class. It has been utilized in many situations such as modeling of growth curves and dose-response modeling. The latter modeling will be the main interest in this study to construct a dose-response relationship, as a function of time to IBD (inflammatory bowel disease) dataset. The dataset comes from a clinical trial with 291 subjects measured during a 7 week period. Both linear and non-linear models are considered. A dose time response model with generalized diffusion function is utilized for the non-linear models. The fit of non-linear models are found to be more flexible than linear models hence able to capture more variability present in the data.Keywords: IBD; longitudinal; linear mixed model; non-linear mixed model. AbstrakDesain studi dimana hasil diukur berulang kali dari waktu ke waktu menghasilkan data longitudinal. Sebagian besar metodologi yang digunakan untuk menganalisis data longitudinal adalah model linear dan model linear umum (generalized linear model) dimana sejumlah linearitas tertentu dipertahankan. Asumsi linearitas ini masih dipandang memiliki keterbatasan dan model non-linear adalah pilihan metode lainnya yang menawarkan fleksibilitas. Model non-linear telah digunakan di berbagai macam situasi seperti model kurva pertumbuhan , model farmakokinetika, dan farmakodinamika, dan model respon-dosis. Model respon-dosis akan menjadi fokus dalam penelitian ini untuk membangun hubungan dosis-respon sebagai fungsi waktu dari data IBD dengan menggunakan model linear dan non-linear. Hasil penelitian menunjukan bahwa model non-linear lebih fleksibel daripada model linear sehingga mampu menangkap lebih banyak variabilitas yang ada di dalam data.Keywords: IBD; longitudinal; model linear; model non-linear.
Rainbow Connection Number on Amalgamation of General Prism Graph Rizki Hafri Yandera; Yanne Irene; Wisnu Aribowo
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (792.789 KB) | DOI: 10.15408/inprime.v1i1.12732

Abstract

AbstractLet  be a nontrivial connected graph, the rainbow-k-coloring of graph G is the mapping of c: E(G)-> {1,2,3,…,k} such that any two vertices from the graph can be connected by a rainbow path (the path with all edges of different colors). The least natural number
Application of Fuzzy K-Nearest Neighbor (FKNN) to Detect the Parkinson’s Disease L.N. Desinaini; Azizatul Mualimah; Dian C. R. Novitasari; Moh. Hafiyusholeh
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.517 KB) | DOI: 10.15408/inprime.v1i1.12827

Abstract

AbstractParkinson’s disease is a neurological disorder in which there is a gradual loss of brain cells that make and store dopamine. Researchers estimate that four to six million people worldwide, are living with Parkinson’s. The average age of patients is 60 years old, but some are diagnosed at age 40 or even younger and the worst thing is some patients are late to find out that they have Parkinson's disease. In this paper, we present a diagnosis system based on Fuzzy K-Nearest Neighbor (FKNN) to detect Parkinson’s disease. We use Parkinson’s disease dataset taken from UCI Machine Learning Repository. The first step is normalize the Parkinson’s disease dataset and analyze using Principal Component Analysis (PCA). The result shows that there are four new factors that influence Parkinson’s disease with total variance is 85.719%. In classification step, we use several percentage of training data to classify (detect) the Parkinson's disease i.e. 50%, 60%, 70%, 75%, 80% and 90%. We also use k = 3, 5, 7, and 9. The classification result shows that the highest accuracy obtained for the percentage of training data is 90% and k = 5, where 19 are correctly classified i.e. 14 positive data and 5 negative data, while 1 positive data is classified incorrectly.Keywords: Parkinson's disease; Fuzzy K-Nearest Neighbor; Principal Component Analysis. AbstrakPenyakit Parkinson merupakan kelainan sel saraf pada otak yang menyebabkan hilangnya dopamin pada otak. Para peneliti mengestimasi bahwa, empat sampai enam juta orang di dunia, menderita Parkinson. Penyakit ini rata-rata diderita oleh pasien berusia 60 tahun, namun beberapa orang terdeteksi saat berusia 40 tahun atau lebih muda dan hal terburuk adalah seseorang terlambat untuk mendeteksinya. Di dalam artikel ini, kami menyajikan sistem diagnosa penyakit Parkinson menggunakan metode Fuzzy K-Nearest Neighbor (FKNN). Kami menggunakan Data uji yang diperoleh dari UCI Machine Learning Repository yang telah banyak diterapkan pada masalah klasifikasi. Tahapan pertama yang kami lakukan adalah menormalisasi data kemudian menganalisisnya menggunakan Analisis Komponen Utama (Principal Component Analysis). Hasil Analisis Komponen Utama menunjukkan bahwa terdapat empat factor baru yang mempengaruhi penyakit Parkinson dengan variansi total 87,719%. Pada tahap klasifikasi, kami menggunakan beberapa prosentase data latih untuk mendeteksi penyakit yaitu 50%, 60%, 70%, 75%, 80% and 90%. Selain itu, kami menggunakan beberapa nilai k yaitu 3, 5, 7, and 9. Hasil menunjukkan bahwa klasifikasi dengan akurasi tertinggi diperoleh untuk 90% data latih dengan k = 5, dimana 19 diklasifikasikan secara tepat yaitu 14 data positif dan 5 data negatif, sedangkan satu data positif tidak diklasifikasikan dengan tepat.Keywords: penyakit Parkinson; Fuzzy K-Nearest Neighbor; Analisis Komponen Utama.
Bounds of Adj-TVaR Prediction for Aggregate Risk Khreshna Syuhada
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2527.467 KB) | DOI: 10.15408/inprime.v1i1.12788

Abstract

In financial and insurance industries, risks may come from several sources. It is therefore important to predict future risk by using the concept of aggregate risk. Risk measure prediction plays important role in allocating capital as well as in controlling (and avoiding) worse risk. In this paper, we consider several risk measures such as Value-at-Risk (VaR), Tail VaR (TVaR) and its extension namely Adjusted TVaR (Adj-TVaR). Specifically, we perform an upper bound for such risk measure applied for aggregate risk models. The concept and property of comonotonicity and convex order are utilized to obtain such upper bound.Keywords:        Coherent property, comonotonic rv, convex order, tail property, Value-at-Risk (VaR).
Statistical Modelling of Extreme Data of Air Pollution in Pekanbaru City Ari Pani Desvina; Elfira Safitri; Ade Novia Rahma
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 1, No 1 (2019)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1036.992 KB) | DOI: 10.15408/inprime.v1i1.12839

Abstract

AbstractAir pollution is a phenomenon that is often discussed, especially regarding air quality in urban areas. This has become a major contributor to health problems and environmental issues in Asian countries, such as Indonesia, especially Riau Province. The event of forest fires is one of the many events that occurred in Indonesia, especially Riau Province which harmed the population of Indonesia and neighboring countries. The phenomenon of forest forestry generally occurs due to a shift in the season towards drought and can occur in areas prone to forest fires. Therefore, it is necessary to know the model of air pollution distribution by Particulate Matter (PM10) in Pekanbaru City. This study aims to obtain the distribution model for daily air pollution PM10 in Pekanbaru City from 2014 to February 2015. Data was taken from three stations i.e. Sukajadi Station, Tampan Station, and Kulim Station. Four distributions will be tested i.e. Log Pearson III distribution, Gumbel distribution, Generalized Pareto Distribution, and Generalized Extreme Value (GEV) distribution. We test the goodness of fit from these distribution using the Kolmogorov-Smirnov and the Anderson-Darling tests. The result shows that the Generalized Extreme Value (GEV) distribution model was better than the Log Pearson III, Gumbel and Generalized Pareto distribution models for modeling city air pollution data Pekanbaru with three stations namely Sukajadi, Tampan, and Kulim.Keywords: Anderson-Darling; Generalized Extreme Value (GEV); Kolmogorov-Smirnov. AbstrakPencemaran udara merupakan satu fenomena yang sering dibicarakan, apalagi mengenai kualitas udara di daerah perkotaan. Hal ini menjadi penyumbang utama tentang masalah kesehatan dan isu lingkungan hidup di negara-negara Asia, seperti Negara Indonesia khususnya Provinsi Riau. Peristiwa kebakaran hutan merupakan salah satu peristiwa yang banyak terjadi di Indonesia khususnya Provinsi Riau yang berdampak negatif  terhadap penduduk Indonesia dan negara tetangga. Fenomena kebarakan hutan pada umumnya terjadi karena adanya pergeseran musim kearah kemarau dan dapat terjadi di daerah rawan kebakaran hutan. Oleh karena itu, perlu diketahui model distribusi pencemaran udara oleh Particulate Matter (PM10) Kota Pekanbaru. Penelitian ini bertujuan untuk mendapatkan model distribusi data harian pencemaran udara oleh Particulate Matter (PM10) Kota Pekanbaru Tahun 2014 sampai Februari 2015 dengan tiga stasiun yaitu stasiun sukajadi, stasiun tampan dan stasiun kulim. Adapun distribusi yang digunakan adalah distribusi Log Pearson III, distribusi Gumbel, Distribusi Generalized Pareto dan distribusi Generalized Extreme Value (GEV). Berdasarkan pembahasan uji kebaikan (Goodness of Fit) yaitu uji Kolmogorov-Smirnov dan Anderson-Darling, maka diperoleh bahwa model distribusi Generalized Extreme Value (GEV) lebih baik dari pada model distribusi Log Pearson III, Gumbel dan Generalized Pareto untuk memodelkan data  pencemaran udara kota Pekanbaru dengan tiga stasiun yaitu Sukajadi, Tampan dan Kulim.Kata Kunci: Anderson-Darling, Generalized Extreme Value (GEV), Kolmogorov-Smirnov

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