Online shopping is growing so rapidly and has attracted millennials in various way. Unfortunately, the discussion regarding the adoption of online shopping in millennial consumers' context with perceived risk application was still limited. Therefore, the purpose of this study was to investigate the effect of performance expectancy, expectation efforts, social influence, facilitation conditions, hedonic motivation, price value, habits, and perceived risks on behavioral intentions and use behavior. This study also discusses the effect of perceived risks on financial risk, performance risk, and privacy risk. This study can be classified as explanatory research with purposive sampling and partial least square as sampling techniques and data analysis. This study was designed to focus on individuals who can be classified as an online shopper with a range of age of 18-35 years old. The results show that the millennial generation is influenced by the social environment and habits in shaping their behavioral intention. Millennial consumers are also proving very concerned about their perceived risk of financial, performance, and privacy issues when doing online shopping. Interestingly, six of the factors studied (performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, price value, and perceived risk) do not have any influence on the intention to use online commerce technology.

Salah satu kegiatan usaha terus berkembang di Indonesia adalah Usaha Mikro, Kecil dan Menengah (UMKM). UMKM mempunyai peran penting dan strategis dalam pembangunan ekonomi nasional. Namun pemanfaatan teknologi pada kegiatan UMKM masih belum optimal. Para pelaku bisnis UMKM masih menemui kendala yang masih ada yaitu misalnya kesulitan dalam proses monitoring pembayaran transaksi, biaya akses internet yang masih cukup relative mahal dan waktu serta usaha lebih untuk memahami teknologi e-business untuk para pelaku bisnis. Penelitian ini bertujuan untuk menganalisis hubungan antar faktor yang berpengaruh terhadap adopsi e-commerce oleh pelaku UMKM di Kota Subang dengan mengguakan pendekatan model Unified Theory of Acceptance and Use of Technology (UTAUT). UTAUT merupakan model untuk menggambarkan faktor-faktor yang mempengaruhi niat dalam menggunakan teknologi internet (e-commerce). Sampel dalam penelitian ini sebanyak 165 pelaku UMKM. Pengolahan data menggunakan SMART PLS. Hasilnya menunjukkan bahwa Pengaruh PE (Performance Expectancy) terhadap BI (Behavioral Intentions), SI (Social Influence) terhadap BI, BI terhadap USE (Use Behavior) maupun FC (Facilitating Conditions) terhadap UB adalah signifikan.

Penelitian ini bertujuan untuk: (1) menganalisis pengaruh kualitas website terhadap kepercayaan untuk bertransaksi pada e- commerce; (2) menganalisis pengaruh kualitas website terhadap keputusan konsumen dalam bertransaksi pada e-commerce; (3) mengetahui pengaruh kepercayaan terhadap transaksi pada e-commerce. Sampel penelitian sebanyak 150 pelanggan e-commerce di Indonesia. Pengumpulan data menggunakan kuesioner online melalui social media (Facebook, Twitter dan Blogs) dengan diukur Skala Likert serta dianalisis dengan Structural Equation Model (SEM). Hasilnya menunjukkan kualitas website memiliki pengaruh signifikan terhadap kepercayaan pelanggan e-commerce namun tidak berpengaruh langsung terhadap keputusan bertransaksi. Kata Kunci: e-commerce, kualitas website, sumber informasi, kepercayaan, keputusan bertransaksi

<p class="Abstrak">Penelitian ini berfokus pada konsumen milenial yang melakukan belanja online produk UMKM pada e-commerce. Konsumen milenial yang semakin meningkat menjadi peluang bagi UMKM dalam target pemasaran dengan mengetahui bagaimana perilaku dalam berbelanja online. Penelitian ini bertujuan untuk mengkonfirmasi adanya hubungan positif dan signifikan antara (1) <em>performance expectancy, </em>(2) <em>effort expectancy</em>, (3) <em>social influence</em>, (4) <em>facilitating condition</em>, (5) <em>hedonic motivation</em>, (6) <em>price value </em>dan (7)<em> habit </em>terhadap <em>behavioral intention</em>. Adanya hubungan positif dan signifikan antara (8) <em>facilitating condition </em>dan (9) <em>habit</em> terhadap <em>use behavior</em>. Serta hubungan positif dan signifikan antara (10)  <em>behavioral intention</em> berpengaruh signifikan terhadap <em>use behavior</em>. Pada hasil penelitian juga khusus melihat bagaimana usia sebagai variabel moderator mempengaruhi dalam adopsi penggunaan <em>e-commerce</em>. Metode pada penelitian ini  menerapkan model <em>Unified Theory of Acceptance and Use of Technology </em>(UTAUT) yang berfokus pada perilaku konsumen. Sampel dalam penelitian ini sebanyak 150 responden milenial. Pengolahan data menggunakan SMART PLS. Hasilnya menunjukkan bahwa ada hubungan positif dan signifikan anatara <em>social influence</em> dan <em>habit </em>terhadap <em>behavioral intenion</em>. Ada hubungan positif dan signifikan anatara <em>facilitating condition </em>dan <em>habit </em>terhadap <em>use behavior</em>. Serta Ada hubungan positif dan signifikan<em> </em>pada <em>behavioral intention</em> terhadap <em>use behavior</em>. Hasil ini bisa menjadi referensi pelaku UMKM untuk mengembangkan pemasaran dengan mengggunakan media sosial karena konsumen milenial memiliki fasilitas untuk mengakses internet  dalam aktifitasnya serta mudah dipengaruhi oleh lingkungan sosialnya.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>This research focuses on millennial consumers who shop online for MSMEs products on e-commerce. Millennial consumers are increasingly becoming an opportunity for MSMEs in marketing targets by knowing how to behave online shopping. This study aims to confirm the existence of a positive and significant relationship between (1) performance expectancy, (2) effort expectancy, (3) social influence, (4) facilitating conditions, (5) hedonic motivation, (6) price value and (7) habit of behavioral intention. There is a positive and significant relationship between (8) facilitating conditions and (9) habits on use behavior. And a positive and significant relationship between (10) behavioral intention has a significant effect on use behavior. The research also specifically looks at how age as a moderating variable influences adoption of e-commerce. The method in this study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) model that focuses on consumer behavior. The sample in this study were 150 millennial respondents. Data processing using PLS SMART. The results show that there is a positive and significant relationship between social influence and habit on behavioral intention. There is a positive and significant relationship between facilitating conditions and habits on use behavior. And there is a positive and significant relationship between behavioral intention and use behavior. These results can be a reference for SMEs to develop marketing by using social media because millennial consumers have the facility to access the internet in their activities and are easily influenced by their social environment.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Penelitian ini bertujuan untuk mengetahui kinerja keuangan perusahaan dalam subsektor properti dan real estate yang terdaftar di Bursa Efek Indonesia (BEI) dengan menggunakan altman Z-Score dan perbedaan harga saham untuk perusahaan dengan kategori sehat dan tidak sehat. Selain itu penelitian ini juga bertujuan untuk mengetahui pengaruh rasio yang ada dalam Altman Z-Score terhadap harga saham. Metode analisis yang digunakan yaitu metode kuantitatif deskriptif menggunkan Altman Z-Score Model untuk mengetahui kinerja keuangan perusahaan dan untuk mengetahui hubungan rasio-rasio dalam Altman Z-Score terhadap harga saham menggunakan metode data panel dengan pendekatan random effect. Hasil penelitian menunjukkan dari 33 sampel yang digunakan didapatkan bahwa pada 2013 sebanyak 58% perusahaan dinyatakan sehat, 24% perusahaan dinyatakan kritis dan 18% lainnya dikategorikan tidak sehat. Pada 2014, 73% dinyatakan sehat 18% dinyataka kritis dan 9% mendapat predikat tidak sehat. Sementara pada 2015 turun menjadi 70% perusahaan dinyatakan sehat. Perusahaan-perusahaan yang memperoleh predikat kritis pada 2015 adalah 18%, perusahaan yang mendapat perdikat tidak sehat sebanyak 12%. Selain itu ada perbedaan harga saham antara perusahaan yang dikategorikan sehat dan tidak sehat. Kaitannya terhadap harga saham menunjukkan bahwa variabel WCTA tidak berpengaruh signifikan terhadap harga saham, RETA berpengaruh signifikan terhadap harga saham, EBITTA dan BVEBVD tidak berpengaruh signifikan terhadap harga saham. Secara simultan WCTA, RETA, EBITTA dan BVEBVD berpengaruh terhadap harga saham akan tetapi hanya 28.20% variasi harga saham dapat dipengaruhi oleh variasi WCTA, RETA, EBITTA, dan BVEBVD secara bersama-sama. Kata Kunci: Kinerja keuangan, perubahan harga saham, Altman Z-Score, Perusahaan properti dan real estate, Bursa Efek Indonesia

Payment of tuition as one of the sources of funds, plays an important role in the sustainability of the operations of higher education. The problem that arises is that students are not often late to make payments in a timely manner. One of the factors causing the many cases of late payment of tuition fees due to lack of policy and decisive action on the part of the campus when students are late in making payments, besides the factors of parents and students also have an influence on the delay. The purpose of this study is to classify students who are late and timely in making SPP payments using the C4.5 algorithm. From the total sample used then divided into 4 partitions, partition 1 for 90% training data and 10% testing data, partition 2 for 80% training data and 20% testing data, and partition 3 for 70% training data and 30% testing data , and partition 4 for 60% training data and 40% testing data. The classification results of the C4.5 algorithm are evaluated and validated with cross validation and confusion matrix to determine the accuracy of the C4.5 algorithm in predicting late SPP payments. Based on the comparison of the results of evaluations and validations conducted, it shows that data partition 2 has a better level of accuracy than the other partitions, which is 75%. Keywords: Data Mining, Decision Tree (C4.5), SPP.

State Polytechnic of Subang (POLSUB) with the core business in education has the main activity in the form of academic management. The main activities and supporters are inseparable from the use of Information and Communication Technology (ICT) equipment, that can help and facilitate work. Current use of ICT, according to Greenpeace, also plays a role in environmental damage. POLSUB as a new state university, wisely applying ICTs requires an IT strategic plan. Strengths-Weakness-Opportunities-Threats (SWOT) and IT-Balance Scorecard (IT-BSC) methods are used to get a strategic plan for the creation of Green Computing in the daily activities of the campus environment. The results of the SWOT analysis obtained a total weight score of Internal Factor Evaluation (IFE) 2.72 representing average internal position and a total score of weight 2.68 External Factor Evaluation (EFE) representing a medium external position. Based on the results of the Threats-Opportunities-Weakness-Strengths (TOWS) matrix analysis obtained ten strategic proposals, which were then grouped into four IT-BSC perspectives. Keywords: Green Computing, Information and Communication Technology, IT-Balanced Scorecard, SWOT.

The Indonesian government established a policy on tuition fees in higher education, namely the Single Tuition System (UKT), including the Subang State Polytechnic (POLSUB) using this system. Currently in POLSUB there are frequent delays in UKT payments, the number of delays is not much but continues to increase every semester. The purpose of this study is to classify students who are late and timely in making UKT payments using the C4.5 algorithm. Based on the comparison of evaluation and validation results from the four partitions shows that the data results from partitions 1 to 4 producing the best accuracy is on the 2nd partition has an accuracy rate of 75%. While the results using genetic algorithms improve accuracy results to 83.64%. In selecting the initial data collection feature, there are 13 features and after testing there are only 7 features that are most influential.