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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika ECSOFiM (Economic and Social of Fisheries and Marine Journal) JFMR (Journal of Fisheries and Marine Research) Jurnal Kelautan dan Perikanan Terapan (JKPT) Jurnal Litbang Polri Inferensi Enthusiastic : International Journal of Applied Statistics and Data Science Xplore: Journal of Statistics Jurnal Sistem Informasi Indonesian Journal of Statistics and Its Applications

Siswantining, Titin

Universitas Indonesia

Author-ID : 2405677

Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions Alhadi Bustamam; Mohamad Irlin Sunggawa; Titin Siswantining
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp773-786

Protein interactions play an essential role in the study of how an organism can be infected with a disease and also its effects. One of the challenges in computational methods in the prediction of protein-protein interactions is how to represent a sequence of amino acids in a vector so that it can be used in machine learning to create a model that can predict whether or not an interaction occurs in a protein pair. This paper examined the qualitative feature encoding methods of amino acid sequence, namely, multivariate mutual information (MMI), and the quantitative feature encoding methods, namely, autocorrelation. We develop the new design for MMI and autocorrelation feature encoding methods which give better results than the previous research. There are four ways to build the MMI method and six ways to build the autocorrelation method that we tested. We also built four types of MMI-autocorrelation (mixed) method and look for the best form of each type of MMI, autocorrelation, and mixed-method. We combine these feature encoding methods with support vector machine (SVM) as machine learning methods. We also test the encoding methods we propose to several machine learning classifier methods, such as random forest (RF), k-nearest neighbor (KNN), and gradient boosting.

PERUBAHAN AKTIVITAS NELAYAN DI URK, PROVINSI FLEVOLAND SETELAH PEMBANGUNAN AFSLUITDIJK Shasa Chairunnisa; Dewi Susiloningtyas; Tuty Handayani; Titin Siswantining
JFMR (Journal of Fisheries and Marine Research) Vol 5, No 1 (2021): JFMR VOL 5 NO.1
Publisher : JFMR (Journal of Fisheries and Marine Research)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jfmr.2021.005.01.5

Afsluitdijk adalah tanggul laut terbesar yang dibangun oleh pemerintah untuk mengatasi masalah banjir di Belanda. Pembangunan Afsluitdijk menyebabkan beberapa perubahan pada lingkungan dan komunitas pesisir di sekitarnya, salah satunya adalah komunitas nelayan di Urk, Provinsi Flevoland, Belanda. Tujuan dari penelitian ini adalah untuk menganalisis perubahan kegiatan penangkapan ikan setelah pembangunan Afsluitdijk. Penelitian ini dilakukan di Desa Urk, Provinsi Flevoland, Belanda pada Mei - Juni 2019. Penelitian ini menggunakan pendekatan deskriptif kualitatif. Data penelitian diperoleh dari wawancara mendalam dan studi literatur. Hasil penelitian menunjukkan adanya perubahan lingkungan akuatik yang awalnya merupakan air asin menjadi air tawar. Terdapat beberapa nelayan yang masih bertahan hidup untuk menangkap ikan di perairan sekitar Urk dan Harlingen setelah pembangunan, tetapi ada juga yang beralih profesi dan pindah ke daerah lain di Belanda. Kesimpulan dari penelitian ini adalah terdapat perubahan pada kondisi lingkungan perairan dan ekonomi nelayan, serta nelayan harus beradaptasi dengan kondisi baru setelah pembangunan Afsluitdijk.

Changes in Ecological and Economic Conditions of Sero Fishermen After Reclamation in Kamal Muara, North Jakarta Dewi Susiloningtyas; Shasa Chairunnisa; Titin Siswantining; Tuty Handayani
ECSOFiM (Economic and Social of Fisheries and Marine Journal) Vol 8, No 1 (2020): ECSOFiM Oktober 2020
Publisher : Faculty of Fisheries and Marine Science, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ecsofim.2020.008.01.02

Reclamation in Kamal Muara North Jakarta changes the surrounding fishery activities. It is estimated that there are changes in the ecological and economic conditions of fishermen. This study aims to analyze the ecological and economic changes that occur in Sero fishermen in Kamal Muara after the reclamation of artificial islands. This research uses a descriptive quantitative approach with a questionnaire, observation, interview, and documentation methods. The results showed that after reclamation, the fishing ground of Sero fishermen moved along the Dadap coast, Tangerang with a distance of less than one nautical mile from the coastline. In addition to fishing ground changes, the results of the paired sample t-test on the catches volume and profit of Sero fishermen showed significant changes after the reclamation of the artificial island. However, operational costs did not show any significant changes between before and after reclamation. This study concludes that there are changes in the ecological and economic conditions of Sero fishermen in Kamal Muara after the reclamation.

PARAMETER POPULASI DAN TINGKAT PEMANFAATAN IKAN KUNIRAN (Upeneus sulphureus, Cuvier 1829) DI PERAIRAN SELAT MALAKA Nurulludin Nurulludin; Titin Siswantining; Muhammad Taufik; Rudy Masuswo Purwoko
Jurnal Kelautan dan Perikanan Terapan (JKPT) Vol 3, No 1 (2020): JKPT Juni 2020
Publisher : Politeknik Ahli Usaha Perikanan

Sumberdaya ikan kuniran (Upeneus sulphureus) di Selat Malaka telah dieksploitasi sejak lama dengan alat tangkap pukat tarik, terutama sebelum adanya moratorium pelarangan alat tangkap trawl dan sejenisnya. Penelitian dilaksanakan pada bulan Januari - November 2014 di Perairan Selat Malaka. Pengukuran panjang cagak ikan kuniran diambil secara acak terhadap 2.694 sampel yang dilakukan di PPS Belawan. Tujuan penelitian ini adalah untuk menganalisis beberapa parameter pertumbuhan populasi ikan kuniran (Upeneus sulphureus) di Selat Malaka. Analisis data parameter populasi dianalisis menggunakan FAO-ICLARM Stock Assessement Tools (FISAT). Hasil analisis diperoleh beberapa parameter populasi ikan kuniran dengan koefisien pertumbuhan (K) sebesar 0,80 per tahun, (L∞) 21,0 cm, (M) 1,73 per tahun (F) 2,51 per tahun, dan E 0,59 per tahun. Penambahan baru individu ke dalam populasi berlangsung sepanjang tahun dan mencapai puncaknya terjadi pada akhir musim timur (Juni – Agustus) sampai musim peralihan II (September – Nopember). Pemanfaatan ikan kuniran di perairan Selat Malaka sebelum moratorium pelarangan pukat tarik dalam kondisi jenuh (Fully exploited).

MULTIPLE IMPUTATION FOR ORDINARY COUNT DATA BY NORMAL DISTRIBUTION APPROXIMATION Titin Siswantining; Muhammad Ihsan; Saskya Mary Soemartojo; Devvi Sarwinda; Herley Shaori Al-Ash; Ika Marta Sari
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.68-78

Missing values are a problem that is often encountered in various fields and must be addressed to obtain good statistical inference such as parameter estimation. Missing values can be found in any type of data, included count data that has Poisson distributed. One solution to overcome that problem is applying multiple imputation techniques. The multiple imputation technique for the case of count data consists of three main stages, namely the imputation, the analysis, and pooling parameter. The use of the normal distribution refers to the sampling distribution using the central limit theorem for discrete distributions. This study is also equipped with numerical simulations which aim to compare accuracy based on the resulting bias value. Based on the study, the solutions proposed to overcome the missing values in the count data yield satisfactory results. This is indicated by the size of the bias parameter estimate is small. But the bias value tends to increase with increasing percentage of observation of missing values and when the parameter values are small.

SPRATAMA MODEL FOR INDONESIAN PARAPHRASE DETECTION USING BIDIRECTIONAL LONG SHORT-TERM MEMORY AND BIDIRECTIONAL GATED RECURRENT UNIT Titin Siswantining; Stanley Pratama; Devvi Sarwinda
MEDIA STATISTIKA Vol 15, No 2 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.2.129-138

Paraphrasing is a way to write sentences with other words with the same intent or purpose. Automatic paraphrase detection can be done using Natural Language Sentence Matching (NLSM) which is part of Natural Language Processing (NLP). NLP is a computational technique for processing text in general, while NLSM is used specifically to find the relationship between two sentences. With the development Neural Network (NN), nowadays NLP can be done more easily by computers. Many models for detecting and paraphrasing in English have been developed compared to Indonesian, which has less training data. This study proposes SPratama Model, which models paraphrase detection for Indonesian using a Recurrent Neural Network (RNN), namely Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The data used is "Quora Question Pairs" taken from Kaggle and translated into Indonesian using Google Translate. The results of this study indicate that the proposed model has an accuracy of around 80% for the detection of paraphrased sentences.

Pengaruh Karakteristik Pasien 4 Diagnosis Penyakit Rawat Inap dengan Biaya Tertinggi di PT Asuransi ABC Terhadap Biaya Rawat Inap Berdasarkan Data Klaim Saskya Mary Soemartojo; Titin Siswantining; Darayani Putri; Mariam Rahmania
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

PT Asuransi ABC in collaboration with 68 companies, consists of 34960 participants, of which there are 1731 participants who filed claims. This study uses secondary data period July 1, 2013 - 30 September 2014. This study focused on inpatient claims, where there are 4 burdensome disease diagnosis PT Asuransi ABC at a high cost, those are coronary atrial diseases, chronic renal failure, typhoid fever, dengue haemorrhagic fever. Multiple correspondence analysis method is used to find the characteristics of each patient's disease diagnosis as well as the tendency of the characteristics of the patients in the cost of hospitalization . From the research, there are differences in patient characteristics between the disease and also the trend in the cost of hospitalization . Furthermore, the multiple linear regression analysis of patient characteristics influence on the cost of hospitalization . From the results of research only typhoid disease hospitalization costs are influenced by patient characteristics .

An Indonesian Adaptation of The Students’ Preparedness for University e-Learning Environment Questionnaire Kasiyah Junus; Lia Sadita; Titin Siswantining; Diana Nur Vitasari
Jurnal Sistem Informasi Vol. 19 No. 1 (2023): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Although most students are digital natives, online learning requires different skills as compared to conventional face-to-face learning. This paper aims to adapt and test the reliability and validity of the Students’ Preparedness for the University e-Learning Environment questionnaire developed by Parkes et al. into Bahasa Indonesia. The original questionnaire covers a wide range of competencies relevant to e-learning preparedness for university e-learning environments in three dimensions. Prior to reliability and validity checking, pilot testing is conducted to test the unidimesionality of the instrument and the rating scale. An item-match analysis test is also carried out to observe the suitability of each item. Then, the final version of the questionnaire is administered to a large representative sample of respondents for whom the questionnaire is intended. The results show that, with a total of 1446 students from a public university in Indonesia as respondents, the adapted questionnaire is valid and reliable.

Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation Titin Siswantining; Kathan Gerry Vivaldi; Devvi Sarwinda; Saskya Mary Soemartojo; Ika Mattasari; Herley Al-Ash
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p1-12

The purpose of this study is to implement the ensemble self-organizing maps (E-SOM) method to impute missing values at the preprocessing data stage, which is an important stage when making predictions or classifications. The Ensemble Self-Organizing Maps (E-SOM) is the development of the SOM imputation method, in which the E-SOM method is implemented by applying an ensemble framework using several SOMs to improve generalization capabilities. In this study, the E-SOM imputation method is implemented in South African heart disease data using random forest as a classification model. The results of the model evaluation showed that for accuracy in testing data, the Random Forest model formed from E-SOM imputed data yields better accuracy values than the Random Forest model formed from SOM-imputed data for variations of 36, 49, 64, and 81 neurons, while for variation of 25 neurons both models produce the same accuracy value. From the variation of the number of ensembles applied, the E-SOM imputation method with a combination of 81 neurons and 15 ensemble numbers produced a Random Forest model with the most optimal value of accuracy.

Evaluation of Biclustering Imputation Methods for Glioblastoma Gene Expression Data Silalahi, Agatha; Titin Siswantining; Setia Pramana
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art7

Glioblastoma is a highly aggressive primary brain tumor with a low survival rate. One of the main challenges in analyzing glioblastoma gene expression data is the presence of missing values, which can reduce biclustering accuracy and affect biological interpretation. This research compared six imputation methods k-nearest neighbors (KNN), mean imputation, singular value decomposition, nonnegative matrix factorization, soft impute, and autoencoderon the GSE4290 gene expression dataset with missing values ranging from 5% to 50%. An evaluation using root mean square error (RMSE), mean absolute error (MAE), and structural similarity index measure (SSIM) showed that soft impute provided the best performance at all levels of missing values, with RMSE of 0.0076, MAE of 0.0073, and perfect SSIM of 1.0000 at 50% missing values. Meanwhile, deep learning-based autoencoder experienced significant performance degradation at high missing values. These findings indicate that more complex models are not always superior, and regularization-based approaches like soft impute are more effective in preserving the biological structure of the data. The results of this research contribute to the optimization of imputation strategies to improve the accuracy of biclustering analysis in glioblastoma studies.

Co-Authors Bustamam, Alhadi Chairunnisa, Shasa Darayani Putri Devvi Sarwinda Dewi Susiloningtyas Dewi Susiloningtyas Diana Nur Vitasari Dwi Purwoko Frans Tjahyono Herley Al-Ash Herley Shaori Al-Ash Ika Marta Sari Ika Mattasari Kasiyah Junus Kathan Gerry Vivaldi Lia Sadita Mariam Rahmania Mastika, Mastika Mohamad Irlin Sunggawa Muhammad Ihsan Muhammad Taufik Nurulludin Nurulludin Pramana, Setia Rudy Masuswo Purwoko Saskya Mary Soemartojo Saskya Mary Soemartojo Shasa Chairunnisa Shasa Chairunnisa Silalahi, Agatha Soemarso, Ditoprasetyo Rusharsono Stanley Pratama Sunandi, Etis Tesdiq Prigel Kaloka Tuty Handayani Tuty Handayani

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