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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Ilmu Pertanian Indonesia Jurnal Penyuluhan MEDIA KONSERVASI Jurnal Manajemen dan Agribisnis FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Media Gizi dan Keluarga Buletin Peternakan Media Statistika Statistika Jurnal Manajemen Teknologi IPTEK The Journal for Technology and Science Jurnal Ilmu Komunikasi Journal The Winners Journal of Economics, Business, & Accountancy Ventura Gadjah Mada International Journal of Business JAM : Jurnal Aplikasi Manajemen Journal of the Indonesian Mathematical Society Jurnal RISET Geologi dan Pertambangan Journal of Regional and City Planning JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Binus Business Review JURNAL HAMA DAN PENYAKIT TUMBUHAN TROPIKA Journal of Economic Education Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Informatika Pertanian BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Agrisocionomics: Jurnal Sosial Ekonomi Pertanian Jurnal Kebijakan Sosial Ekonomi Kelautan dan Perikanan KEK (Kajian Ekonomi dan Keuangan) STI Policy and Management Journal JURNAL PANGAN FIBONACCI: Jurnal Pendidikan Matematika dan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Matematika (Statmat) MEANS (Media Informasi Analisa dan Sistem) Jurnal Risalah Kebijakan Pertanian dan Lingkungan BISNIS & BIROKRASI: Jurnal Ilmu Administrasi dan Organisasi JURNAL ILMIAH GLOBAL EDUCATION Malcom: Indonesian Journal of Machine Learning and Computer Science Xplore: Journal of Statistics STATISTIKA Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Society Media Penelitian dan Pengembangan Kesehatan Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education eJEBA
ASEP SAEFUDDIN
Departemen Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Institut Pertanian Bogorm, Kampus IPB Darmaga, Bogor 16688
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Systematic Literature Review of Competitive Advantage and Marketing Capability of Small Medium Enterprises (SMEs) Hanny Nurlatifah; Asep Saefuddin; Marimin Marimin; Heny Suwarsinah
Journal of Economics, Business, and Accountancy Ventura Vol. 24 No. 2 (2021): August - November 2021
Publisher : Universitas Hayam Wuruk Perbanas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14414/jebav.v24i2.2797

Abstract

The discussion about the formation of competitive advantage in work organizations such as SMEs is still not widely discussed. The current literature still discusses marketing activities in general, not specifically for SMEs. This article aims to find out the factors that influence SMEs' competitive advantage and marketing capabilities. The literature review method systematically uses three stages. First planning for selecting source articles, second implementing and reporting stage. The PRISMA Literature Review Model selects articles and data visualization using VOS Viewer software. The findings of this article are the related potential relationships between marketing capabilities as forming competitive advantages for small and medium enterprises. Eleven topics are frequently discussed in a collection of journals, and the dominant words are sustainable marketing orientation, marketing, and Company Performance. The three groups can be grouped into personality development, business management, and abilities. Differences in the types of business groups and business sizes as differentiators of business performance results are not widely seen in article searches. These findings suggest further research to examine business groups' role and size in determining SMEs' competitive advantage and marketing capabilities.
Induksi Mutasi pada Stek Pucuk Anyelir (Dianthus caryophyllus Linn.) melalui Iradiasi Sinar Gamma Syarifah Iis Aisyah; Hajrial Aswidinnoor; Asep Saefuddin; Budi Marwoto; Sarsidi Sastrosumarjo
Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) Vol. 37 No. 1 (2009): Jurnal Agronomi Indonesia
Publisher : Indonesia Society of Agronomy (PERAGI) and Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (620.001 KB) | DOI: 10.24831/jai.v37i1.1396

Abstract

It has been a common knowledge that LD50 is commonly used in estimating optimal doses of gamma irradiation in a breeding program. This research was aimed at observing radiosensitivity of five carnation's genotypes to gamma irradiation, to find the LD50 of carnation's cuttings, and to obtain solid mutants from five numbers of Carnation.  For cuttings, carnation genotype number 10.8 was the most insensitive to gamma rays, whereas number 24.15 was the most sensitive one.  LD50 of carnation's cuttings were obtained around 49 -72 gray. There were 19 mutants produced from this treatment. The desired mutans were mostly produced from the treated 24.1 genotype whereas the character mutans were mostly observed in MV2 generation. Irradiation treatment on genotype 24.1 produced most stabile mutans while the less was in genotype 24.14.  The produced mutants were qualitatively different in colour and petal shape, and stabile till third generation.   Key words: LD50, gamma irradiation, induced mutation, carnation.
AN APPLICATION OF GENETIC ALGORITHM FOR CLUSTERING OBSERVATIONS WITH INCOMPLETE DATA Frisca Rizki Ananda; Asep Saefuddin; Bagus Sartono
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v1i1.48

Abstract

Cluster analysis is a method to classify observations into several clusters. A common strategy for clustering the observations uses distance as a similarity index. However distance approach cannot be applied when data is not complete. Genetic Algorithm is applied by involving variance (GACV) in order to solve this problem. This study employed GACV on Iris data that was introduced by Sir Ronald Fisher. Clustering the incomplete data was implemented on data which was produced by deleting some values of Iris data. The algorithm was developed under R 3.0.2 software and got satisfying result for clustering complete data with 95.99% sensitivity and 98% consistency. GACV could be applied to cluster observations with missing value without filling in the missing value or excluding these observations. Performance on clustering incomplete observations is also satisfying but tends to decrease as the proportion of incomplete values increases. The proportion of incomplete values should be less than or equal to 40% to get sensitivity and consistency not less than 90. Keywords: Cluster Analysis, Genetic Algorithm, Incomplete Data.
THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL Nuramaliyah Nuramaliyah; Asep Saefuddin; Muhammad Nur Aidi
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.564

Abstract

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.
PEMODELAN STATISTICAL DOWNSCALING DENGAN LASSO DAN GROUP LASSO UNTUK PENDUGAAN CURAH HUJAN M. Yunus; Asep Saefuddin; Agus M Soleh
Indonesian Journal of Statistics and Applications Vol 4 No 4 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i4.724

Abstract

One of the rainfall prediction techniques is the Statistical Downscaling Modeling (SDS). SDS modeling is one of the applications of modeling with covariates conditions that are generally large and not independent. The problems that will be encountered is the problem of ill-conditional data i.e multicollinearity and the high correlation between variables. The case of highly correlated data causes a linear regression coefficient estimators obtained to have a large variance. This research was conducted to make the statistical downscaling modeling using the lasso and group lasso for the prediction of rainfall. Group of the covariate scenario is applied based on the adjacent area, the high correlation between covariates and correlation between covariates and responses, and also the addition of dummy variables. Scenario six (grouping which is done by considering the covariates that have a positive correlation to the response is divided into 3 groups, 1 individual and the covariates that are negatively correlated with the response are divided into 2 groups, 1 individual) is better than the other scenarios in linear modeling without a dummy. Then, linear modeling with a dummy is better than without a dummy for both techniques. In linear modeling with a dummy, the Group lasso technique can be considered more in SDs modeling, because the difference in the RMSEP statistical value and the correlation coefficient value is significant.
Stochastic Residual Selection in Simulated Annealing for Clusterwise Panel Optimization Luh Putu Widya Adnyani; Bagus Sartono; Asep Saefuddin; I Made Sumertajaya; Gerry Alfa Dito
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7608

Abstract

Modeling heterogeneity in panel data requires solving a complex combinatorial partition problem under structural constraints. Although clusterwise regression captures latent group structures with distinct parameters, determining the optimal partition remains computationally challenging due to the vast solution space and susceptibility to local minima. This study proposes a modified simulated annealing (SA) algorithm incorporating a Stochastic Residual Selection (SRS) mechanism, in which candidate units are selected from a high-residual subset rather than deterministically relocating only the unit with the largest residual. The stochastic candidate-size parameter was evaluated using m=1 and m=5, where m=1 represents deterministic selection of the largest residual unit, while m=5 randomly selects one unit from the five largest-residual units for clusterreassignment. The stochastic perturbation enhances global exploration and improves convergence stability in non-convex optimization landscape. Simulation experiments involving 200 individuals observed over three time periods demonstrate that the proposed SRS-SA outperforms standard SA, achieving an Adjusted Rand Index of approximately 0.95 at 1,000 iterations while producing lower Mean Absolute Bias and Mean Squared Error. An empirical application to improved sanitation data across districts and municipalities in Java, Indonesia, further confirms its effectiveness in identifying latent structural heterogeneity. These findings highlight the robustness and computational efficiency gained through stochastic diversification in metaheuristic optimization for constrained clusterwise panel modeling.
Co-Authors . Marzuki . Sutriyati Achmad ACHMAD . Achmad Ramzy Tadjoedin adwendi, satria june Agus M Soleh Agus Mohamad Soleh Agustifa Zea Tazliqoh Ahmad A. Mattjik Ahmad Ansori Mattjik Aji H. Wigena Aji Hamim Wigena Alif Supandi Alinda F. M. Zain Alkahfi, Cahya Ananda Shafira Anang Kurnia Andres Purmalino Ani Suryani Anik Djuraidah Arief Daryanto Arista Marlince Tamonob Arman Arman Arman Arman Arman Arman Arman Arman Arnita Arnita Azagi, Ilham Alifa Bagus Sartono Bambang Indriyanto Basita Ginting Budhi Purwandaya, Budhi Budi Marwoto Budi Susetyo Bunasor Sanim Cece Sumantri Chalid Talib Citra Jaya Daowen Zhang Dede Dirgahayu Domiri Dede Dirgahayu Domiri, Dede Dirgahayu Dewi Juliah Ratnaningsih Diah Krisnatuti Dian Handayani Dian Kusumaningrum Dian Kusumaningrum, Doni Suhartono Dudung Darusman Eka Intan Kumala Putri Embay Rohaeti Eminita, Viarti Enny Kristiani Enny Kristiani Erfiani Erfiani Erfiani Eri Purnomohadi Etih Sudarnika Etty Riani Euis Sunarti Eva Z Yusuf Fatah Sulaiman Fitrah Ernawati Frisca Rizki Ananda Fulazzaky, Tahira Gerry Alfa Dito H. R. Eddie Gurnadi HAJRIAL ASWIDINNOOR Hanny Nurlatifah Harapin Hafid H. Hardiansyah . Hardinsyah Hari Wijayanto Hartoyo, harry Hasnataeni, Yunia Hendra Prasetya Hengki Muradi Heny Suwarsinah Hermanto Siregar Hidayat Syarief Hilman Dwi Anggana Husaini . I Made Sumertajaya I Wayan Mangku Ida Mariati Hutabarat Indahwati Itasia Dina Sulvianti Jajang Jajang Jodi Vanden Eng Joko Affandi Joko Affandi Joko Sutrisno JOKO SUTRISNO Khairil Anwar Notodiputro Kristiani, Enny Kusman Sadik Lia Budimulyati Salman Lia Ratih Kusuma Dewi Lilik Noor Yuliati Lismayani Usman Luh Putu Widya Adnyani Lukmanul Hakim Lukmanul Hakim M. Yunus M. Yunus Maghfiroh, Firda Aulia Mangara Tambunan Margono Slamet Marimin , Marizsa Herlina Marliati . Marliati Marliati Mirnawati Sudarwanto Muggy David Cristian Ginzel Muhammad Nur Aidi Muradi, Hengki Musa Hubeis mutiah, siti Ni Nyoman Sawitri Nimmi Zulbainarni Ningsih, Wiwik Andriyani Lestari Ninuk Purnaningsih Nirawita Untari Nunung Nuryartono Nuramaliyah, Nuramaliyah Nurul Hidayati Nusar Hajarisman Pang S. Asngari Pien Budiyanto Prabowo Tjitropranoto Pradina, Fathia Anggriani Priyadi Kardono Purnomohadi, Eri R. Ruswandi Rahmadi Sunoko Rahmadi Sunoko Ratna Megawangi Rimun Wibowo Ristu Haiban Hirzi, Ristu Rita Kusriastuti Rita Rahmawati Rizal Syarief Rizal Syarief Rizka Rahmaida Ronny Rachman Noor Rudy Priyanto S. Damanhur, Didin Santun R.P. Sitorus SANTUN R.P. SITORUS Sarah Putri Sarsidi Sastrosumarjo Sausan Nisrina Setiadi Djohar Setiawan Setiawan Siti Sundari Sitti Nurhaliza Sjafri Mangkuprawira Sjafri Mangkuprawira Soedijanto Padmowihardjo Soekirman Soekirman Soetrisno Hadi Sony Sunaryo Sri Yusnita Burhan Suhartono Suhartono . Sumardjo Sumarjo Gatot Irianto Sumartono Sumartono Sutarman Sutarman . Syafri Mangkuprawira Syafri Mangkuprawira Syarifah Iis Aisyah TADJOEDIN, ACHMAD RAMZY Tagor Alamsyah Harahap Talib, Chalid Tati Rajati Tati Suprapti Tiyas Yulita triguna, gunadi Ujang Sumarwan Umi Cahyaningsih Upik Kesumawati Hadi Utami Dyah Syafitri Wahida Ainun Mumtaza William A. Hawley Wiwik Andriyani Lestari Ningsih Yani Nurhadryani Yekti Widyaningsih Yenni Angraini Yudhistira Arie Wijaya Yuni Ros Bangun Yusuf, Eva Z Zinggara hidayat