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Faktor-Faktor Yang Mempengaruhi Waktu Penyusunan Tugas Akhir Mahasiswa S1 (Studi Kasus: Mahasiswa FMIPA Unsyiah) Salwa, Nany
GRADIEN : Jurnal Ilmiah MIPA Vol 10, No 2 (2014): Juli 2014
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (592.503 KB)

Tugas akhir (TA) merupakan salah satu mata kuliah yang wajib diselesaikan oleh setiap mahasiswa untuk menyandang gelar sarjana. Penelitian ini bertujuan melihat faktor -faktor yang berpengaruh terhadap lamanya penyusunan tugas akhir mahasiswa Fakultas MIPA Unsyiah. Data yang digunakan dalam penelitian ini adalah data primer yaitu dengan cara menyebarkan kuisioner kepada responden secara langsung dan dikirim melalui email. Data tersebut kemudian dibangkitkan dengan menggandakan data berjumlah 6 kali data aslinya, 9 kali data aslinya dan 20 kali data aslinya untuk memudahkan dalam menginterpretasikan data. Responden pada penelitian ini adalah mahasiswa lulusan FMIPA Unsyiah jurusanÂ Matematika, Fisika, Kimia, dan BiologiÂ dari angkatan 2004 sampai dengan 2008. Analisis yang digunakan adalah metode CHAID (Chi-Square Automatic Interaction Detection). Hasil dari analisis CHAID pada data 312 diprediksikan bahawa faktor-faktor yang mempengaruhi lama penyusunan tugas akhir adalah bahan penelitian, mata kuliah dan aktifitas selain mengerjakan tugas akhir. Pada data 468 diprediksikan bahwa faktor-faktor yang mempengaruhi lama penyusunan tugas akhir adalah bahan penelitian, mata kuliah, aktifitas selain mengerjakan tugas akhir, dan motivasi. Dan pada data 1040 diprediksikan bahwa faktor-faktor yang mempengaruhi lama penyusunan tugas akhir adalah bahan penelitian, kesehatan, ide penelitian, motivasi, dan aktifitas selain mengerjakan tugas akhir. Jadi, dapat disimpulkan bahwa prediksi faktor yang paling signifikan yang mempengaruhi lamanya penyusunan tugas akhir adalah bahan penelitian yang digunakan sebagai referensi oleh mahasiswa.

Analisis Diskriminan dalam Menentukan Fungsi Pengelompokan Kabupaten/Kota di Indonesia berdasarkan Indikator Indeks Pembangunan Manusia Nurhasanah, Nurhasanah; Salwa, Nany; Ornila, Lyra; AR, Fitriana; Hasan, Amiruddin
Jurnal EMT KITA Vol 5, No 1 (2021): Journal EMT KITA
Publisher : Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/emt.v5i1.320

The Human Development Index (HDI) is a measure used to measure the success of human development in an area. There are several indicators used to compile the HDI value. Previously, regencies/cities were grouped based on the HDI indicator. The grouping is done using K-means cluster analysis with 4 categories, namely regencies/cities that have low, medium, high, and very high HDI indicator values. From the results of determining the category of the HDI indicator in an area, we need a function that can be used to classify an object into one of the HDI indicator value categories. The compilation of the classification function is carried out using discriminant analysis. The results obtained from the discriminant analysis are that there are 10 variables or indicators that fall into the discriminant function. The resulting discriminant function is quite good in classifying each group with a success rate of more than 85% and the discriminant function is supported by a fairly good validation success rate, namely with a classification accuracy of 93.20%.

Clustering Regency/City in Indonesia based on Human Development Index Indicators Nurhasanah Nurhasanah; Nany Salwa; Lyra Ornila
Proceedings of AICS - Social Sciences Vol 7 (2017): 7th AIC in conjuction ICMR 2017 Universitas Syiah Kuala October 2017
Publisher : Proceedings of AICS - Social Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (124.471 KB)

Human Development Index (HDI) is an index that measures the success rate of human development in a region. Development undertaken by the government aims to create a prosperous and peaceful society. The problems that often arise in the development process are the uneven development in every regency/city in Indonesia. This can be seen from the value of HDI is still different in each regency/city in Indonesia. The purpose of this study is to categorize regency/cities in Indonesia based on indicators of HDI. The clustering method is K-means cluster analysis. The data used is the data indicator HDI Indonesia 2015 obtained from BPS website every province. The results of the analysis formed 4 clusters. The first cluster consisted of 20 regency with average HDI indicator is very low, the second cluster consisted of 148 regency/city with average HDI indicator is medium, the third cluster consisted of 88 regency/city with average HDI indicator is very high, and the fourth cluster consists of 258 regency/city with HDI indicators is very high.

Factors Affecting the Migration Decision of Tsunami Survivors from the Relocation Area Saiful Mahdi; Nany Salwa; Cut Mardiana
Proceedings of AICS - Social Sciences Vol 7 (2017): 7th AIC in conjuction ICMR 2017 Universitas Syiah Kuala October 2017
Publisher : Proceedings of AICS - Social Sciences

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The government has undertaken a relocation program to address the housing problems caused by the earthquake and tsunami of December 26, 2004 in Aceh. However, the relocation of tsunami survivors also caused other impacts after the relocation was carried out. If the relocation site is not as desirable, it can cause tsunami survivors to decide to out-migrate the relocation area. This study was conducted to determine factors that significantly affect the decision of migration of tsunami survivors from the relocation area. This research is expected to provide information on post-disaster relocation for consideration in making relocation policy in the future. We use classification tree, which is part of the CART (Classification and Regression Trees) method. The data used is from The Aftermaid of Aid (AoA) survey conducted by International Center for Aceh and Indian Ocean Studies (ICAIOS) and Earth Observatory of Singapore (EOS) during 2014-2015. The results of the research indicate the factors that significantly affect the migration decision of tsunami survivors from a relocation area are gender, age, and job before relocation, total assets, and the distance of relocation house to central market, house modification, and concerns about possible of future tsunami. Most affecting factor of migration decision, however, is the distance of relocation house to the central market, confirming the impact of sosio-economic factor of post-disaster relocation. The optimal classification tree obtained has a classification accuracy rate of 85, 64%.

PENENTUAN KARAKTERISTIK PARIWISATA DAN MODEL JUMLAH WISATAWAN UNTUK KABUPATEN/KOTA DI PROVINSI ACEH Nurhasanah Nurhasanah; Nany Salwa; Nelva Amelia
Jurnal Natural Volume 16, Number 1, March 2016
Publisher : Universitas Syiah Kuala

Tourism is one of the primary sectors that is expected to increase the regional government income. Therefore there is a need to observe the factors that affect the successfulness of tourism factors and products offered. Tourism products can be tourist destinations, where the characteristics of that particular destination can affect the decisions made by the tourist to return the place again. The characteristics of tourism in Aceh can be analyzed by using biplot analysis. Meanwhile, the effects of tourism characteristics on the number of tourists in Aceh from the year 2008 until 2013 is analyzed using panel data regression analysis that is approached by Fixed Effext Model (FEM). Based on the biplot graph, the cities that are superior in their number of all tourism products are Sabang and Banda Aceh. Cities other than these two cities tend to have a lower number of their tourism products. The biplot graph can explain the relationship between the variables of tourism products by 83.8%. Based on the model of fixed effect panel data, Aceh tourism products that affect the number of tourists in Aceh is the number of accommodations, restaurants, and tourist attractions. Fixed effect model explain correlation between the variables of tourism products to the number of tourists in Aceh by 78.8%.

PENENTUAN KARAKTERISTIK PARIWISATA DAN MODEL JUMLAH WISATAWAN UNTUK KABUPATEN/KOTA DI PROVINSI ACEH Nurhasanah Nurhasanah; Nany Salwa; Nelva Amelia
Jurnal Natural Volume 16, Number 1, March 2016
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v16i1.4805

Tourism is one of the primary sectors that is expected to increase the regional government income. Therefore there is a need to observe the factors that affect the successfulness of tourism factors and products offered. Tourism products can be tourist destinations, where the characteristics of that particular destination can affect the decisions made by the tourist to return the place again. The characteristics of tourism in Aceh can be analyzed by using biplot analysis. Meanwhile, the effects of tourism characteristics on the number of tourists in Aceh from the year 2008 until 2013 is analyzed using panel data regression analysis that is approached by Fixed Effext Model (FEM). Based on the biplot graph, the cities that are superior in their number of all tourism products are Sabang and Banda Aceh. Cities other than these two cities tend to have a lower number of their tourism products. The biplot graph can explain the relationship between the variables of tourism products by 83.8%. Based on the model of fixed effect panel data, Aceh tourism products that affect the number of tourists in Aceh is the number of accommodations, restaurants, and tourist attractions. Fixed effect model explain correlation between the variables of tourism products to the number of tourists in Aceh by 78.8%.

Perbandingan Perilaku Gempa Bumi Susulan Pidie Jaya (7 Desember 2016) dan Ambon (26 September 2019) Anwar, Samsul; Maulia, Faiza; Salwa, Nany
Jurnal Lingkungan dan Bencana Geologi Vol 13, No 2 (2022)
Publisher : Badan Geologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34126/jlbg.v13i2.349

Aceh dan Maluku merupakan dua wilayah yang terletak pada pertemuan lempeng tektonik dunia sehingga banyak mengalami kejadian gempa bumi. Gempa bumi utama pada umumnya diikuti oleh gempa bumi susulan dengan kekuatan dan intensitas yang berbeda-beda. Gempa bumi Pidie Jaya (Provinsi Aceh) pada tanggal 7 Desember 2016 memiliki kesamaan karakter dengan gempa bumi Ambon (Provinsi Maluku) pada tanggal 26 September 2019. Kedua gempa bumi tersebut memiliki magnitudo 6,5 dengan kedalaman yang dangkal (< 60 km). Meskipun kedua gempa bumi utama tersebut memiliki karakter yang sama, namun kerusakan dan korban jiwa yang diakibatkan keduanya berbeda sehingga perlu dilakukan perbandingan perilaku gempa bumi susulan di kedua wilayah. Penelitian ini bertujuan untuk membandingkan perilaku gempa bumi susulan pasca gempa bumi utama di Pidie Jaya tahun 2016 dan Ambon tahun 2019. Penelitian ini menggunakan tiga fungsi lifetime analysis dalam menganalisis perilaku gempa bumi susulan, yaitu probability density function (PDF), reliabilitas, dan hazard. Data yang digunakan bersumber dari BMKG berupa selisih waktu terjadinya gempa bumi susulan terhadap gempa bumi utamanya. Melalui graphical analysis, diketahui bahwa data selisih waktu gempa bumi susulan Pidie Jaya mengikuti distribusi lognormal, sedangkan data selisih waktu gempa bumi susulan Ambon mengikuti distribusi weibull. Hasil penelitian menunjukkan bahwa gempa bumi susulan Pidie Jaya berpeluang besar untuk terjadi dengan intensitas yang tinggi pada 2 hari pertama setelah gempa bumi utamanya, sedangkan gempa bumi susulan Ambon cenderung terjadi dengan intensitas yang lebih merata selama periode pengamatan. Hasil pengujian log rank test membuktikan adanya perbedaan yang signifikan antara kurva reliabilitas gempa bumi susulan di wilayah Pidie Jaya dan Ambon. AbstractAceh and Maluku are two areas located at the confluence of the world's tectonic plates so that they experience many earthquakes. Mainshocks are typically followed by aftershocks with different magnitudes and intensities. The Pidie Jaya (Aceh Province) earthquake on December 7, 2016, has similar character with the Ambon (Maluku Province) earthquake on September 26, 2019. Both earthquakes have magnitudes of 6.5 with a shallow depth (< 60 km). Although those earthquakes possessed a similar character, the damage and casualties caused were different, so it is necessary to compare the aftershocks behavior following the mainshock in those two regions. This study aims to compare the aftershocks behavior following the mainshock of Pidie Jaya in 2016 and Ambon in 2019. This study utilizes three lifetime analysis functions in analyzing the aftershocks behavior, namely the probability density function (PDF), reliability and hazard function. The data employed sourced from BMKG, namely the time difference between aftershocks and the mainshock. Through graphical analysis, it is known that the time difference data for Pidie Jaya aftershocks follows a lognormal distribution, while the time difference data for Ambon aftershocks follows a weibull distribution. The results showed that the Pidie Jaya aftershock had a high probability of occurring with high intensity in the first 2 days after the mainshock, while the Ambon aftershock tends to happen with more even intensity during the observed periods. The log rank test results prove there is a significant difference between the reliability curves of the aftershocks in the Pidie Jaya and Ambon regions.

Analisis Faktor-Faktor Yang Memengaruhi Keluhan Kesehatan Di Indonesia Menggunakan Metode Regresi Data Panel Rahmayuni, Titin; AR, Fitriana; Salwa, Nany; Nurhasanah, Nurhasanah
Transcendent Journal of Mathematics and Applications Vol 3, No 2 (2024)
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/tjoma.v3i2.43045

Health complaints are one aspect of the problem that is the focus of attention in Indonesia. Over the past 3 years, the percentage of health complaints in Indonesia has increased. One of the ways that can be done to reduce the problem of health complaints is to find out the factors that influence it. This study aims to find a suitable model to determine the factors that influence health complaints in Indonesia. To find out the factors that influence health complaints in Indonesia, this study uses a panel data regression analysis method with 3 basic models, namely the Common Effect Model (CEM), Fixed Effect Model (FEM), Random Effect Model (REM). The data in this study consists of cross section data in the form of 34 provinces in Indonesia and time series data in the form of data from 2020 to 2022 with 1 dependent variable and 9 independent variables. The results showed that the selected model of health complaints in Indonesia was the Random Effect Model with individual effects. This model produces an R2 value of 58.19% and an adjusted R2 value of 54.09%. This study obtained 3 significant variables, namely the sick population who did not seek treatment, the population who self-medicated, and the birth process that was not assisted by health workers. Meanwhile, the variables that have a positive effect are the sick population that does not seek treatment, women with marital status using family planning, and population who smoke.

Classifying Regencies and Cities on Human Development Index Dimensions: Application of K-means Cluster Analysis Nurhasanah, Nurhasanah; Salwa, Nany; Ornila, Lyra; Hasan, Amiruddin; Mardhani, Martahadi
Jurnal Sains Sosio Humaniora Vol. 5 No. 2 (2021): Volume 5, Nomor 2, Desember 2021
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jssh.v5i2.15753

The Human Development Index (HDI) is a measurement that analyzes a region's development in improving human development. The government's development plan aims to create a successful and peaceful life. The unbalanced development in every regency and city in Indonesia is a typical issue during the development process. It may also be shown that the HDI level changes across regencies and cities in Indonesia. This research aims to identify Indonesian regencies and cities based on HDI indices. K-Means clustering algorithm is the clustering method adopted. The results of the analysis formed 4 clusters. The first cluster consisted of 20 regencies with a low average HDI indicator. The second cluster consisted of 148 regencies and cities with an average HDI indicator is medium. The third cluster consisted of 88 regencies and cities with an average HDI indicator. The fourth cluster consists of 258 regencies and cities with high HDI indicators

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